Showing papers by "Dong Liu published in 2023"
TL;DR: In this paper , the authors bring in the recently introduced deep image prior (DIP) and merge it with electrical impedance tomography (EIT) reconstruction to regularize EIT reconstruction problems by compelling the recovered image to be synthesized from a given NN architecture.
Abstract: Neural networks (NNs) have been widely applied in tomographic imaging through data-driven training and image processing. One of the main challenges in using NNs in real medical imaging is the requirement of massive amounts of training data – which are not always available in clinical practice. In this article, we demonstrate that, on the contrary, one can directly execute image reconstruction using NNs without training data. The key idea is to bring in the recently introduced deep image prior (DIP) and merge it with electrical impedance tomography (EIT) reconstruction. DIP provides a novel approach to the regularization of EIT reconstruction problems by compelling the recovered image to be synthesized from a given NN architecture. Then, by relying on the NN's built-in back-propagation and the finite element solver, the conductivity distribution is optimized. Quantitative results based on simulation and experimental data show that the proposed method is an effective unsupervised approach capable of outperforming state-of-the-art alternatives.
2 citations
TL;DR: In this paper , Fe3O4/Co-MOF composites were fabricated via a facile and scalable co-precipitation process, and the microstructure and morphology of the composites are characterized and the combination of hollow Fe 3O4 and Co-MOFs was found to contribute towards the dissipation of incident waves due to the magnetic-dielectric synergistic effect.
2 citations
TL;DR: In this paper , the effects of plasma on flame characteristics and soot formation were investigated, and it was found that the overall temperature increased somewhat with plasma activation, but remained roughly constant when the discharge frequency or applied voltage increased.
1 citations
TL;DR: Wang et al. as mentioned in this paper proposed the use of corn stalk coke to remove mercury from coal-fired flue gas and through elemental sulfur (CSC-S), water vapor activation, and sulfur modification combined with CSC-H2O-S to prepare three different adsorbents.
Abstract: It is necessary to find a better method to remove mercury from coal-fired flue gas. This work proposes the use of corn stalk coke to remove mercury from coal-fired flue gas and through elemental sulfur (CSC-S), water vapor activation (CSC-H2O), and sulfur modification combined with water vapor activation (CSC-H2O-S) to prepare three different adsorbents. The mercury removal performance of the adsorbents prepared by different methods was evaluated on a small fixed-bed mercury removal experimental platform. The experimental results showed that the order of mercury removal efficiency of the four adsorbents was: CSC-H2O-S > CSC-S > CSC-H2O > CSC. Brunauer–Emmett–Teller (BET), Fourier transform infrared (FTIR) spectroscopy, and X-ray photoelectron spectroscopy (XPS) were adopted to study the physical and chemical properties of the adsorbent surface and the mechanism of mercury removal. The results showed that water vapor activation can improve the pore structure of the adsorbent, increase its specific surface area, and generate new oxygen-containing functional groups on the surface of the adsorbent. The adsorption kinetic model further demonstrated that the water vapor activation process can improve the physical adsorption performance of corn stalk char, and the sulfur modification process can improve the chemical adsorption performance of corn stalk char. Quantum chemical studies have shown that the surface structure doped with S and O atoms is conducive to enhancing the adsorption of Hg0.
1 citations
TL;DR: GraphGPSM as discussed by the authors is a global scoring model based on equivariant graph neural network (EGNN) to guide protein structure modeling and ranking for protein folding and ranking.
Abstract: The scoring models used for protein structure modeling and ranking are mainly divided into unified field and protein-specific scoring functions. Although protein structure prediction has made tremendous progress since CASP14, the modeling accuracy still cannot meet the requirements to a certain extent. Especially, accurate modeling of multi-domain and orphan proteins remains a challenge. Therefore, an accurate and efficient protein scoring model should be developed urgently to guide the protein structure folding or ranking through deep learning. In this work, we propose a protein structure global scoring model based on equivariant graph neural network (EGNN), named GraphGPSM, to guide protein structure modeling and ranking. We construct an EGNN architecture, and a message passing mechanism is designed to update and transmit information between nodes and edges of the graph. Finally, the global score of the protein model is output through a multilayer perceptron. Residue-level ultrafast shape recognition is used to describe the relationship between residues and the overall structure topology, and distance and direction encoded by Gaussian radial basis functions are designed to represent the overall topology of the protein backbone. These two features are combined with Rosetta energy terms, backbone dihedral angles, and inter-residue distance and orientations to represent the protein model and embedded into the nodes and edges of the graph neural network. The experimental results on the CASP13, CASP14, and CAMEO test sets show that the scores of our developed GraphGPSM have a strong correlation with the TM-score of the models, which are significantly better than those of the unified field score function REF2015 and the state-of-the-art local lDDT-based scoring models ModFOLD8, ProQ3D, and DeepAccNet etc. The modeling experimental results on 484 test proteins demonstrate that GraphGPSM can greatly improve the modeling accuracy. GraphGPSM is further used to model 35 orphan proteins and 57 multi-domain proteins. The results show that the average TM-score of the models predicted by GraphGPSM is 13.2% and 7.1% higher than that of the models predicted by AlphaFold2. GraphGPSM also participates in CASP15 and achieves competitive performance in global accuracy estimation.
1 citations
TL;DR: In this article , a lateral swirl combustion system (LSCS) was proposed to improve the fuel/air mixing and combustion processes, and previous results have validated its excellent combustion performance in direct injection diesel engines.
TL;DR: In this paper , a light-weight, low volume, low-cost, easy-to-use and lowmaintenance mini Infrared Lidar (mIRLidar) sensor is developed for the first time.
Abstract: In order to conduct more thorough research on the structural characteristics of the atmosphere and the distribution and transmission of atmospheric pollution, the use of remote sensing technology for multi-dimensional detection of the atmosphere is needed. A light-weight, low-volume, low-cost, easy-to-use and low-maintenance mini Infrared Lidar (mIRLidar) sensor is developed for the first time. The model of lidar is established, and the key optical parameters of the mIRLidar are optimized through simulation, in which wavelength of laser, energy of pulse laser, diameter of telescope, field of view (FOV), and bandwidth of filter are included. The volume and weight of the lidar system are effectively reduced through optimizing the structural design and designing a temperature control system to ensure the stable operation of the core components. The mIRLidar system involved a 1064 nm laser (the pulse laser energy 15 μJ, the repetition frequency 5 kHz), a 100 mm aperture telescope (the FOV 1.5 mrad), a 0.5 nm bandwidth of filter and an APD, where the lidar has a volume of 200 mm × 200 mm × 420 mm and weighs about 13.5 kg. It is shown that the lidar can effectively detect three-dimensional distribution and transmission of aerosol and atmospheric pollution within a 5 km detection range, from Horizontal, scanning and navigational atmospheric measurements. It has great potential in the field of meteorological research and environmental monitoring.
TL;DR: In this article , a method of matching the combustion chamber diameter for lateral swirl combustion system (LSCS), at engine speeds of 1800 r/min and 2500 r/m, under 25%-100% loads and excess air coefficients (φa) of 1.2-2.0, was presented.
TL;DR: In this paper , the effect of hydrogen and ammonia addition on soot production in ethylene laminar inverse diffusion flames (IDF) was numerically examined for the first time, and the experimental results from the literature were used to validate the temperature and soot volume fraction profiles.
Abstract: Zero-carbon alternative fuels such as hydrogen and ammonia are gaining popularity. Blending these fuels with hydrocarbons is an intermediate approach to mitigate soot and carbon dioxide production. Recent studies have concentrated their attention to the effects of ammonia and hydrogen on the soot production of hydrocarbon fuels. It is still necessary to completely comprehend how this influence is dependent on the type of fuel and flame configuration. In this work, the effect of hydrogen and ammonia addition on soot production in ethylene laminar inverse diffusion flames (IDF) was numerically examined for the first time. The thermal, chemical, and combined effects of hydrogen and ammonia were assessed by fictitious species. The experimental results from the literature were used to validate the temperature and soot volume fraction profiles. Results indicated that the flame temperature and the production of radicals are promoted chemically by hydrogen addition but inhibited under the thermal effect of ammonia. The principal source of the reduction in OH-represented flame height, soot volume fraction, average diameter, and primary particle number density in the IDF is the thermal effect of additives, and hydrogen addition performs better than ammonia. The major and intermediate species, the aromatic hydrocarbons, and the soot formation or oxidation reaction rates are decreased mainly by the hydrogen and ammonia thermal effect while increased moderately by the chemical effect. Therein, the reduction in soot generation is mainly due to the polycyclic aromatic hydrocarbon condensation rate being slowed down by additives. The inhibition is attributed to the thermal effect of additions, and hydrogen behaves better than ammonia.
TL;DR: Wang et al. as mentioned in this paper proposed GraphPLBR, a framework based on Graph Convolutional Neural (GCN) networks, to predict protein-ligand binding residues (PLBR), where proteins are represented as a graph with residues as nodes through 3D protein structure data, such that the PLBR prediction task is transformed into a graph node classification.
Abstract: The intermolecular interactions between proteins and ligands occur through site-specific amino acid residues in the proteins, and the identification of these key residues plays a critical role in both interpreting protein function and facilitating drug design based on virtual screening. In general, the information about the ligands-binding residues on proteins is unknown, and the detection of the binding residues by the biological wet experiments is time consuming. Therefore, many computational methods have been developed to identify the protein-ligand binding residues in recent years. We propose GraphPLBR, a framework based on Graph Convolutional Neural (GCN) networks, to predict protein-ligand binding residues (PLBR). The proteins are represented as a graph with residues as nodes through 3D protein structure data, such that the PLBR prediction task is transformed into a graph node classification task. A deep graph convolutional network is applied to extract information from higher-order neighbors, and initial residue connection with identity mapping is applied to cope with the over-smoothing problem caused by increasing the number of graph convolutional layers. To the best of our knowledge, this is a more unique and innovative perspective that utilizes the idea of graph node classification for protein-ligand binding residues prediction. By comparing with some state-of-the-art methods, our method performs better on several metrics.
TL;DR: In this article , the performance of S-lidars with Si/InGaAs arrays was analyzed under conditions of wide variability of optical weather and sky background brightness. But the authors focused on the performance analysis of the S-Lidar instrument and the external background source, taking into account their wide variability.
Abstract: The article proposes a methodology for analyzing the performance of S-lidars (S comes from Scheimpflug) as a new class of environmental remote sensors operating under conditions of wide variability of optical weather and sky background brightness. The novelty of the problem statement, the methods used and the results obtained are determined by their application to laser sensing systems with unconventional design principles and the consequent need to revise the traditional ways of assessing their potential capabilities. The research method is based on a dimensionless-parametric approach, which allows comparing phenomena and systems of different scales and combining complementary characteristics and parameters. Effects of the dimensionless optical weather factor on lidar potential are shown being investigated under various environmental conditions, from the clear atmosphere through haze and mist to fog when probing in Vis/SWIR spectral bands and using Si/InGaAs detector arrays. It is shown exactly how and to what extent the significant differences in their spectral sensitivity and internal noise parameters are susceptible to the wide spectral and energy variability of the sky background brightness observed at very different angles to the Sun. A detailed analysis of the two most important influencing factors within the system, “S-Lidar instrument + Optical weather + External background source”, taking into account their wide variability, allowed us to describe their joint nonlinear influence and, thus, to anticipate the imposed limitations. The proposed dimensionless-parametric concept for predicting the potential capabilities of S-lidars with Si/InGaAs arrays is aimed at expanding applications of this rapidly developing class of remote sensors in a wide variety of environments.
TL;DR: DeepUMQA3 as discussed by the authors is a web server for evaluating protein complex structures using deep neural network, where features are extracted from three levels of overall complex, intra-monomer, and intermonomer and a improved deep residual neural network is used to predict per-residue lDDT and interface residue accuracy.
Abstract: Model quality assessment is a crucial part of protein structure prediction and a gateway to proper usage of models in biomedical applications. Many methods have been proposed for assessing the quality of structural models of protein monomers, but few methods for evaluating protein complex models. As protein complex structure prediction becomes a new challenge, model quality assessment methods that can provide accurate evaluation of complex structures are urgently required. Here, we present DeepUMQA3, a web server for evaluating protein complex structures using deep neural network. For an input complex structure, features are extracted from three levels of overall complex, intra-monomer, and inter-monomer, and a improved deep residual neural network is used to predict per-residue lDDT and interface residue accuracy. DeepUMQA3 ranks first in the blind test of interface residue accuracy estimation in CASP15, with Pearson, Spearman and AUC of 0.564, 0.535 and 0.755 under the lDDT measurement, which are 18.5%, 23.6% and 10.9% higher than the second-best method, respectively. DeepUMQA3 can also accurately assess the accuracy of all residues in the entire complex and distinguish high- and low-precision residues/models. The websever of DeepUMQA3 are freely available at http://zhanglab-bioinf.com/DeepUMQA_server/.
TL;DR: A novel bamboo-shaped N-doped graphyne encapsulating Fe nanoparticles (Fe-N-graphyne) is firstly developed, and the two-dimensional graphyne was changed into one-dimensional tubular structure for the first time as discussed by the authors .
TL;DR: In this paper , the formation and evolution profiles of soot containing morphology and nanostructures in ethylene inverse diffusion flames with different ozone concentration additions were experimentally studied, and the surface chemistry and oxidation reactivity of the soot particles were also compared.
Abstract: Ozone is a prospective additive for enhancing and controlling combustion under lean or very lean conditions, and reduces NOx and particulate matter emissions simultaneously. Typically, in studying the effects of ozone on combustion pollutants, the focus is on the final yield of pollutants, while its detailed effects on the soot formation process remain unknown. Here, the formation and evolution profiles of soot containing morphology and nanostructures in ethylene inverse diffusion flames with different ozone concentration additions were experimentally studied. The surface chemistry and oxidation reactivity of soot particles were also compared. The soot samples were collected by a combination of the thermophoretic sampling method and deposition sampling method. High-resolution transmission electron microscopy analysis, X-ray photoelectron spectroscopy and thermogravimetric analysis were applied to obtain the soot characteristics. The results showed that soot particles experienced inception, surface growth, and agglomeration in the ethylene inverse diffusion flame within a flame axial direction. The soot formation and agglomeration were slightly advanced since the ozone decomposition contributed to promoting the production of free radicals and active substances in the ozone added flames. The diameter of primary particles in the flame with ozone addition was larger. With the increase of ozone concentration, the content of soot surface oxygen increased and the ratio of sp2/sp3 decreased. Furthermore, the addition of ozone increased the volatile content of soot particles and improved soot oxidation reactivity.
TL;DR: Huang et al. as discussed by the authors proposed a method for prediction based on the local structural similarity of lncRNA-protein interaction (LPI) network, which computes the local structure similarity of network space and maps it to LPI space, and uses an innovative algorithm that combined Resource Allocation and improved collaborative filtering algorithm to calculate the potential LPI.
Abstract:
The interaction between RNA and protein plays an important role in life activities. Long ncRNAs (lncRNAs) are large non-coding RNAs, and have received extensive attention in recent years. Because the interaction between RNA and protein is tissue-specific and condition-specific, it is time-consuming and expensive to predict the interaction between lncRNA and protein based on biological wet experiments.
The contribution of this paper is to propose a method for prediction based on the local structural similarity of lncRNA-protein interaction (LPI) network.
The contribution of this paper is to propose a method for prediction based on the local structural similarity of lncRNA-protein interaction (LPI) network.
The method computes the local structure similarity of network space, and maps it to LPI space, and uses an innovative algorithm that combined Resource Allocation and improved Collaborative Filtering algorithm to calculate the potential LPI.
AUPR and AUC are significantly better than the five popular baseline methods. In addition, the case study shows that some results of LPLSG prediction on the actual data set have been verified by NPInterV4.0 database and some literatures.
TL;DR: In this paper , an unsupervised deep learning method for positron emission tomography reconstruction (PET) from incomplete data was proposed, which utilizes the deep image prior (DIP) as an untrained deep convolutional neural network (CNN) to generate object reconstructions.
Abstract: In this paper, we propose an unsupervised deep learning method for positron emission tomography reconstruction (PET) from incomplete data. This method utilizes the so-called deep image prior (DIP) as an untrained deep convolutional neural network (CNN) to generate object reconstructions. The main idea is to re-parameterize the image reconstruction problem as a neural network optimization problem. We show that the proposed method effectively addresses the incomplete data reconstruction problem, which otherwise degrades the image resolution and quality of standard reconstruction algorithms. Meanwhile, the proposed method does not require any pre-training procedures, i.e., it is not biased toward any particular dataset. Hence, it has the potential to be used in clinical situations, where training data would be infeasible or prohibitively expensive. The performance of the proposed approach is evaluated with noisy synthetic data based on shepp-logan and brainweb phantoms, and clinical naive rat data. In addition, robustness studies of the approach with respect to regularization parameters are also carried out. We showcase that the proposed method considerably outperforms the state-of-the-art methods, leading to flexible reconstruction from incomplete PET data.
TL;DR: In this article , the authors used the exponential smoothing method, empirical orthogonal decomposition (EOF), correlation analysis, and other methods to explore the influence factors on longwave radiation (OLR) data.
Abstract: Outgoing longwave radiation (OLR) data are one of the key factors in studying the radiation balance of the earth–atmosphere system in East Asia. It is of great significance to explore the influence factors on OLR. This paper processes the data of nearly 19 years, from September 2002 to February 2022, and conducts in-depth research using the exponential smoothing method, empirical orthogonal decomposition (EOF), correlation analysis, and other methods. We found that the spatial distribution of OLR is zonal symmetry and gradually decreases with the increase of latitude. Using EOF analysis, it is found that the total variance contribution of the first four decomposed spatial features exceeds 70%, and the overall change trend of the four-time coefficients in the past 19 years all show a downward trend. OLR is positively correlated with total column water vapor (TCWV), air temperature (AT), and cloud top temperature (CTT), but negatively correlated with cloud top pressure (CTP). OLR has a similar spatial correlation distribution with TCWV and AT, while the spatial correlation between OLR and CTP is opposite to the first two parameters. In most parts of East Asia, the spatial correlation with CTT exceeds 0.8. The change in OLR value is affected by various meteorological parameters. In East Asia, the positive correlation between 30° N and 60° N is significantly affected by TCWV, AT, and CTT; and the negative correlation is more significantly affected by CTP. At 0–25° N, the positive correlation is significantly affected by CTP and CTT, while the negative correlation is significantly affected by TCWV and AT.
TL;DR: In this article , a fitting method of inverting the ozone concentration profile using ultraviolet differential CCD imaging lidar is proposed, and the effect of three different types of aerosol extinction coefficient, three different type of ozone concentration, and five different types for retrieving ozone concentrations was analyzed using simulation.
Abstract: Ozone near the surface of the atmosphere directly stimulates the human respiratory tract and affects human health. In recent years, ozone pollution in China has become a serious problem, so controlling ozone pollution is an urgent task. Differential absorption lidar is a useful tool for detecting ozone concentration, but it cannot receive complete signals in the lower hundreds of meters because of the overlap factor. CCD imaging lidar technology can effectively solve this problem. A fitting method of inverting the ozone concentration profile using ultraviolet differential CCD imaging lidar is proposed in this paper. The effect of three different types of aerosol extinction coefficient, three different types of ozone concentration, and five different types of aerosol wavelength index on retrieving ozone concentrations was analyzed using simulation. For clean aerosol, the relative error of the retrieved ozone concentration is less than 5%. As to polluted aerosol, the relative error of the retrieved ozone concentration is less than 10%. As to heavily polluted aerosol, the relative error of the retrieved ozone concentration is less than 25%. The results show that the larger the value of the aerosol extinction coefficient, the larger the relative error of the retrieved ozone concentration; meanwhile, the lower the ozone concentration, the larger the relative error of the retrieved ozone concentration; at the same time, the further the aerosol wavelength index deviates from 1, the larger the relative error of the retrieved ozone concentration. The relative error of the retrieved ozone concentration in this case was about 4%. It is shown that this fitting method of retrieving ozone concentrations is reasonable and feasible.
10 May 2023
TL;DR: In this paper , a single channel F-P etalon is used as a narrow-band filter to lock the transmittance of 355 nm wavelength emitted laser by control the seed laser with 1064 nm wavelength.
Abstract: Lidar is an important device for detecting atmospheric parameters. In this paper, a single channel F-P etalon is used as a narrow-band filter to lock the transmittance of 355 nm wavelength emitted laser by control the transmittance of seed laser with 1064 nm wavelength. Not only the background noise can be suppressed and the signal-to-noise ratio can be improved, but also the transmittance of the locked target in the etalon can be tracked to achieve higher detection accuracy. During the experiment, transmittance is controlled at target transmittance with 0.01 precision.
TL;DR: In this paper , a set of avalanche photodiode (APD) detectors is independently designed to detect the weak echo signal of 800-900 nm band, and the optical focusing system of the APD detector is demonstrated in the laboratory.
Abstract: Realizing the integrated acquisition and identification of the elevation information and spectral information of the observation target is at the frontier and a future trend of Earth observation technology. This study designs and develops a set of airborne hyperspectral imaging lidar optical receiving systems and investigates the detection of the infrared band echo signal of the lidar system. A set of avalanche photodiode (APD) detectors is independently designed to detect the weak echo signal of 800–900 nm band. The actual radius of the photosensitive surface of the APD detector is 0.25 mm. We design and demonstrate the optical focusing system of the APD detector in the laboratory and obtain that the image plane size of the optical fiber end faces of the APD detector from channel 47 to channel 56 is close to 0.3 mm. Results show that the optical focusing system of the self-designed APD detector is reliable. On the basis of the focal plane splitting technology of the fiber array, we couple the echo signal of 800–900 nm band to the corresponding APD detector through the fiber array and conduct a series of test experiments for the APD detector. Field test results of the ground-based platform show that the APD detectors in all channels can complete the remote sensing measurement of 500 m. The development of this APD detector solves the problem of hyperspectral imaging under weak light signals and realizes the accurate detection of ground targets in the infrared band by airborne hyperspectral imaging lidar.
TL;DR: In this paper , the scale effect exerted a non-monotonic effect on flame temperature and flame carbonization degree, and the authors investigated the impact of scale effect on soot formation and combustion characteristics using optical and sampling diagnostic methods.
Abstract: Understanding the mechanism of scale effect was of great significance for both fundamental theory and actual combustion regulation. Using five combustors of different diameters (4, 6, 7, 8, and 10 mm), this study experimentally investigated the impact of scale effect on soot formation and combustion characteristics using optical and sampling diagnostic methods. For the optical results of two-color pyrometry, the scale effect exerted a non-monotonic effect on flame temperature. The flame temperature increased firstly with the enlargement of the combustor diameter from 4 to 7 mm, but it decreased rapidly when the combustor diameter continued to increase from 7 to 10 mm. However, the soot concentration increased at first and then remained constant approximately with the enlargement of combustor diameter, which exhibited a different tendency from flame temperature. For the sampling results of TEM and TGA, fringe tortuosity and oxidation rate of soot decreased with the combustor diameter enlargement, indicating the higher soot carbonization degree from the larger scale. Furthermore, for the result of exhaust gas analysis, the C2H4 concentration decreased first and then increased with the enlargement of combustor diameter, which also proved that the scale effect exerted a non-monotonic effect on the combustion process.
TL;DR: GraphCPLMQA as discussed by the authors is a graph-coupled network that uses embeddings from protein language models to assess residue-level protein model quality, which consists of a graph encoding module and a transform-based convolutional decoding module.
Abstract: Model quality evaluation is crucial part of protein structural biology. How to distinguish high-quality models from low-quality models, and to assess which high-quality models have relatively incorrect regions for improvement, are remain challenge. More importantly, the quality assessment of multimer models is a hot topic for structure predicton.In this work, we present GraphCPLMQA, a novel graph-coupled network that uses embeddings from protein language models to assess residue-level protein model quality. The GraphCPLMQA consists of a graph encoding module and a transform-based convolutional decoding module. In encoding module, the underlying relational representations of sequence and high-dimensional geometry structure are extracted by protein language models with Evolutionary Scale Modeling. In decoding module, the mapping connection between structure and quality are inferred by the representations and low-dimensional features. Specifically, the triangular location and residue level contact order features are designed to enhance the association between the local structure and the overall topology. Experimental results demonstrate that GraphCPLMQA using single-sequence embedding achieves the best performance compared to the CASP15 interface evaluation method in 9108 models of CASP15 multimer test set. In CAMEO blind test (2022-05-20∼2022-08-13), GraphCPLMQA ranked first compared to other servers. GraphCPLMQA also outperforms state-of-the-art methods on 19,035 models in CASP13 and CASP14 monomer test set. Finally, on AlphaFold2 datasets, GraphCPLMQA was superior to self-assessment of AlphaFold2 in MAE metric, and it was able to screen out better models than AlphaFold2.
TL;DR: In this article , the effect of sintering temperature on phase structure was investigated and it was shown that the phase transformation of ceramics occurs with the change of sinting temperature.
Abstract: Dielectric materials with excellent energy storage performance are crucial to the development of renewable energy. In this work, we prepared 0.95NaNbO 3 –0.05Bi(Zn[Formula: see text]Zr[Formula: see text])O 3 (0.95NN–0.05BZZ) ceramics using solid state sintering and investigated the effect of sintering temperature on phase structure. We find that the phase transformation of ceramics occurs with the change of sintering temperature. The cubic phase (Pm -3[Formula: see text]) and the antiferroelectric phase (Pbma) coexist at 1150[Formula: see text]C, the ferroelectric phase ([Formula: see text]21ma) appears at 1200[Formula: see text]C and its phase proportion decreases with the sintering temperature increasing from 1200[Formula: see text]C to 1280[Formula: see text]C. Finally, we achieve the high recoverable energy storage density ([Formula: see text]) 0.74 J/cm 3 and efficiency ([Formula: see text]) 71% (at 140 kV/cm) at 1150[Formula: see text]C.
TL;DR: DeepAssembly as discussed by the authors uses a population-based evolutionary algorithm to assemble multi-domain proteins by inter-domain interactions inferred from a developed deep learning network, and protein complexes are assembled by means of domains rather than chains using DeepAssembly.
Abstract: Accurately capturing domain-domain interactions is key to understanding protein function and designing structure-based drugs. Although AlphaFold2 has made a breakthrough on single domain, it should be noted that the structure modeling for multi-domain protein and complex remains a challenge. In this study, we developed a multi-domain and complex structure assembly protocol, named DeepAssembly, based on domain segmentation and single domain modeling algorithms. Firstly, DeepAssembly uses a population-based evolutionary algorithm to assemble multi-domain proteins by inter-domain interactions inferred from a developed deep learning network. Secondly, protein complexes are assembled by means of domains rather than chains using DeepAssembly. Experimental results show that on 219 multi-domain proteins, the average inter-domain distance precision by DeepAssembly is 22.7% higher than that of AlphaFold2. Moreover, DeepAssembly improves accuracy by 11.8% for 164 multi-domain structures with low confidence deposited in AlphaFold database. We apply DeepAssembly for the prediction of 247 heterodimers. We find that DeepAssembly generates models with acceptable quality (DockQ ≥ 0.23) for 32.4% of the dimers, suggesting a lighter way to assemble complex structures by treating domains as assembly units and using inter-domain interactions learned from monomer structures.
TL;DR: In this article , a range of glucose sensing materials, including hollow Ni(OH)2@CuS (H-Ni(OH), core-shell Cu2[email protected](OH), and hollow CuS(H-CuS), have been developed at room temperature.
TL;DR: In this article , the authors analyzed the particulate emission characteristics of a lateral swirl combustion system (LSCS) and compared it with the TCDCS at different conditions, showing that the LSCS presents better combustion performance and lower total particle emission characteristics.
TL;DR: In this paper , a generalized methodology to justify the selection of a set of non-energetic S-lidar parameters for a wide range of applications and distance scales, from a bench-top test bed to a 10-km path, was proposed.
Abstract: In recent years, the applications of lidars for remote sensing of the environment have been expanding and deepening. Among them, continuous-wave (CW) range-resolved (RR) S-lidars (S comes from Scheimpflug) have proven to be a new and promising class of non-contact and non-perturbing laser sensors. They use low-power CW diode lasers, an unconventional depth-of-field extension technique and the latest advances in nanophotonic technologies to realize compact and cost-effective remote sensors. The purpose of this paper is to propose a generalized methodology to justify the selection of a set of non-energetic S-lidar parameters for a wide range of applications and distance scales, from a bench-top test bed to a 10-km path. To set the desired far and near borders of operating range by adjusting the optical transceiver, it was shown how to properly select the lens plane and image plane tilt angles, as well as the focal length, the lidar base, etc. For a generalized analysis of characteristic relations between S-lidar parameters, we introduced several dimensionless factors and criteria applicable to different range scales, including an S-lidar-specific magnification factor, angular function, dynamic range, “one and a half” condition, range-domain quality factor, etc. It made possible to show how to reasonably select named and dependent non-energetic parameters, adapting them to specific applications. Finally, we turned to the synthesis task by demonstrating ways to achieve a compromise between a wide dynamic range and high range resolution requirements. The results of the conducted analysis and synthesis allow increasing the validity of design solutions for further promotion of S-lidars for environmental remote sensing and their better adaptation to a broad spectrum of specific applications and range scales.
TL;DR: In this article , N, S co-doped porous carbons are prepared through a two-step calcination process, in which pre-carbonized chitosan, thiourea and potassium hydroxide are served as carbon source, dopant and activator, respectively.