Deep Learning Accelerates the Discovery of Two- Dimensional Catalysts for Hydrogen Evolution Reaction

Two-dimensional materials with active sites are expected to replace platinum as large-scale hydrogen production catalysts.However,the rapid discovery of excellent two-dimensional hydrogen evolution reaction catalysts is seriously hindered due to the long experiment cycle and the huge cost of high-throughput calculations of adsorption energies.Considering that the traditional regression models cannot consider all the potential sites on the surface of catalysts,we use a deep learning method with crystal graph convolutional neural networks to accelerate the discovery of high-performance two-dimensional hydrogen evolution reaction catalysts from two-dimensional materials database,with the prediction accuracy as high as 95.2%.The proposed method considers all active sites,screens out 38 high performance catalysts from 6,531 two-dimensional materials,predicts their adsorption energies at different active sites,and determines the potential strongest adsorption sites.The prediction accuracy of the two-dimensional hydrogen evolution reaction catalysts screening strategy proposed in this work is at the density-functional-theory level,but the prediction speed is 10.19 years ahead of the high-throughput screening,demonstrating the capability of crystal graph convolutional neural networks-deep learning method for efficiently discovering high-performance new structures over a wide catalytic materials space.

An ensemble learning classifier to discover arsenene catalysts with implanted heteroatoms for hydrogen evolution reaction

Accurate regulation of two-dimensional materials has become an effective strategy to develop a wide range of catalytic applications.The introduction of heterogeneous components has a significant impact on the performance of materials,which makes it difficult to discover and understand the structure-property relationships at the atomic level.Here,we developed a novel and efficient ensemble learning classifier with synthetic minority oversampling technique(SMOTE) to discover all possible arsenene catalysts with implanted heteroatoms for hydrogen evolution reaction(HER).A total of 850 doped arsenenes were collected as a database and 140 modified arsenene materials with different doping atoms and doping sites were identified as promising candidate catalysts for HER,with a machine learning prediction accuracy of 81%.Based on the results of machine learning,we proposed 13 low-cost and easily synthesized two-dimensional Fe-doped arsenene catalytic materials that are expected to contribute to high-efficient HER.The proposed ensemble method achieved high prediction accuracy,but millions of times faster to predict Gibbs free energies and only required a small amount of data.This study indicates that the presented ensemble learning classifier is capable of screening high-efficient catalysts,and can be further extended to predict other two-dimensional catalysts with delicate regulation.

Diffusion-tensor imaging as an adjunct to dynamic contrastenhanced MRI for improved accuracy of differential diagnosis between breast ductal carcinoma in situ and invasive breast carcinoma

Objective： To determine the value of diffusion-tensor imaging （DTI） as an adjunct to dynamic contrastenhanced magnetic resonance imaging （DCE-MRI） for improved accuracy of differential diagnosis between breast ductal carcinoma in situ （DCIS） and invasive breast carcinoma （IBC）. Methods： The MRI data of 63 patients pathologically confirmed as breast cancer were analyzed. The conventional MRI analysis metrics included enhancement style, initial enhancement characteristic, maximum slope of increase, time to peak, time signal intensity curve （TIC） pattern, and signal intensity on FS- T2WI. The values of apparent diffusion coefficient （ADC）, directionally-averaged mean diffusivity （D^vg）, exponential attenuation （EA）, fractional anisotropy （FA）, volume ratio （VR） and relative anisotropy （RA） were calculated and compared between DCIS and IBC. Multivariate logistic regression was used to identify independent factors for distinguishing IBC and DCIS. The diagnostic performance of the diagnosis equation was evaluated using the receiver operating characteristic （ROC） curve. The diagnostic efficacies of DCE- MRI, DWI and DTI were compared independently or combined. Results： EA value, lesion enhancement style and TIC pattern were identified as independent factor for differential diagnosis of IBC and DCIS. The combination diagnosis showed higher diagnostic efficacy than a single use of DCE-MRI （P=0.02）, and the area of the curve was improved from 0.84 （95% CI, 0.67-0.99） to 0.94 （95% CI, 0.85-1.00）. Conclusions： Quantitative DTI measurement as an adjunct to DCE-MRI could improve the diagnostic performance of differential diagnosis between DCIS and IBC compared to a single use of DCE-MRI.

Comparative analysis between endometrial proteomes of pregnant and non-pregnant ewes during the peri-implantation period

Background： Early pregnancy failure has a profound impact on both human reproductive health and animal production. 2/3 pregnancy failures occur during the peri-implantation period; however, the underlying mechanism（s） remains unclear. Well-organized modification of the endometrium to a receptive state is critical to establish pregnancy Aberrant endometrial modification during implantation is thought to be largely responsible for early pregnancy loss. Result： In this study, using well-managed recipient ewes that received embryo transfer as model, we compared the endometrial proteome between pregnant and non-pregnant ewes during implantation period. After embryo transfer, recipients were assigned as pregnant or non-pregnant ewes according to the presence or absence of an elongated conceptus at Day 17 of pregnancy. By comparing the endometrial proteomic profiles between pregnant and non-pregnant ewes, we identified 94 and 257 differentially expressed proteins （DEPs） in the endometrial caruncular and intercaruncular areas, respectively. Functional analysis showed that the DEPs were mainly associated with immune response, nutrient transport and utilization, as well as proteasome-mediated proteolysis. Conclusion： These analysis imply that dysfunction of these biological processes or pathways of DEP in the endometrium is highly associated with early pregnancy loss. In addition, many proteins that are essential for the establishment of pregnancy showed dysregulation in the endometrium of non-pregnant ewes. These proteins, as potential candidates, may contribute to early pregnancy loss.

Vision for energy material design:A roadmap for integrated data-driven modeling

The application scope and future development directions of machine learning models(supervised learning, transfer learning, and unsupervised learning) that have driven energy material design are discussed.

Identification of lymph node metastasis by computed tomography in early gastric cancer

Objective:Lymph node status is critical when selecting treatment methods for patients with early gastric cancer(EGC).The aim of this study was to assess the diagnostic value of computed tomography(CT)for detection of lymph node metastasis(LNM)in patients with EGC.Methods:We retrospectively analyzed patients who had pathologically confirmed EGC between November2010 and January 2019.After 1:1 propensity score matching,65 patients with LNM and 65 patients without LNM were retained for comparison.The long diameter(LD)and short diameter(SD)of all visualized lymph nodes in all stations were recorded.The diagnostic value of LNM was assessed with receiver operating characteristic analysis.Results:Among 130 patients,we found a total of 558 lymph nodes on the CT images.Among the diagnostic indicators,the number,sum of LD and sum of SD of lymph nodes greater than 3 mm had better discrimination.The areas under the curve were all greater than 0.75.As for different regions,the optimal cutoff values of number,the sum of LD and sum of SD were determined as follows:overall,≥4,19.9 mm and 13.5 mm;left gastric artery basin,≥3,15.7 mm and 8.6 mm;right gastroepiploic artery basin,≥2,8.6 mm and 7.0 mm.Conclusions:CT is valuable for diagnosing LNM in EGC patients.The number,sum of LD and sum of SD of lymph nodes greater than 3 mm are preferable indicators.Different regional lymph nodes have different optimal criteria for predicting LNM in ECG patients.

The proteome of IVF-induced aberrant embryo-maternal crosstalk by implantation stage in ewes

Background:Implantation failure limits the success of in vitro fertilization and embryo transfer(IVF-ET).Wellorganized embryo-maternal crosstalk is essential for successful implantation.Previous studies mainly focused on the aberrant development of in vitro fertilized(IVF)embryos.In contrast,the mechanism of IVF-induced aberrant embryo-maternal crosstalk is not well defined.Results:In the present study,using ewes as the model,we profiled the proteome that features aberrant IVF embryo-maternal crosstalk following IVF-ET.By comparing in vivo(IVO)and IVF conceptuses,as well as matched endometrial caruncular(C)and intercaruncular(IC)areas,we filtered out 207,295,and 403 differentially expressed proteins(DEPs)in each comparison.Proteome functional analysis showed that the IVF conceptuses were characterized by the increased abundance of energy metabolism and proliferation-related proteins,and the decreased abundance of methyl metabolism-related proteins.In addition,IVF endometrial C areas showed the decreased abundance of endometrial remodeling and redox homeostasis-related proteins;while IC areas displayed the aberrant abundance of protein homeostasis and extracellular matrix(ECM)interaction-related proteins.Based on these observations,we propose a model depicting the disrupted embryo-maternal crosstalk following IVF-ET:Aberrant energy metabolism and redox homeostasis of IVF embryos,might lead to an aberrant endometrial response to conceptus-derived pregnancy signals,thus impairing maternal receptivity.In turn,the suboptimal uterine environment might stimulate a compensation effect of the IVF conceptuses,which was revealed as enhanced energy metabolism and over-proliferation.Conclusion:Systematic proteomic profiling provides insights to understand the mechanisms that underlie the aberrant IVF embryo-maternal crosstalk.This might be helpful to develop practical strategies to prevent implantation failure following IVF-ET.

Correlation between imaging features on computed tomography and combined positive score of PD-L1 expression in patients with gastric cancer

Objective:To explore the correlation between computed tomography(CT)features and combined positive score(CPS)of programmed cell death ligand 1(PD-L1)expression in patients with gastric cancer(GC).Methods:This study reviewed an institutional database of patients who underwent GC operation without neoadjuvant chemotherapy between December 2019 and September 2020.The CPS results of PD-L1 expression of postoperative histological examination were recorded by pathology.Baseline CT features were measured,and their correlation with CPS 5 or 10 score groups of PD-L1 expression was analyzed.Results:Data for 153 patients with GC were collected.Among them,124 were advanced GC patients,and 29were early GC patients.None of the CT features significantly differed between CPS groups with a cutoff score of 5and a score of 10 in patients with early GC.In advanced GC,the presence of lymph nodes with short diameters>10mm was significantly different(P=0.024)between the CPS<5 and CPS≥5 groups.CT features such as tumor attenuation in the arterial phase,long and short diameter of the largest lymph node,the sum of long diameter of the two largest lymph nodes,the sum of short diameter of the two largest lymph nodes,and the presence of lymph nodes with short diameters>10 mm significantly differed between the CPS<10 and CPS≥10 groups in advanced GC.The sensitivity,specificity and area under receiver operating characteristic(ROC)curve of logistic regression model for predicting CPS≥10 was 71.7%,50.0%and 0.671,respectively.Microsatellite instability(MSI)status was significantly different in CPS groups with cutoff score of 5 and 10 in advanced GC patients.Conclusions:CT findings of advanced GC patients with CPS≥10 showed greater arterial phase enhancement and larger lymph nodes.CT has the potential to help screen patients suitable for immunotherapy.

Corrosion and Its Inhibition in Water-Based Drilling Fluid Used in Onshore Oilfied

This study deals with onshore oilfield drilling fluid system to drill corrosion. Corrosion under static and dynamic conditions of the drilling fluid was evaluated. The effect of temperature and pressure on corrosion was examined. Corrosion was more severely affected by temperature compared to pressure. The combined effect of a deoxidizing agent and an inhibitor was observed to exhibit better protection against corrosion by the drilling fluid.

Clarification of the dispersion mechanism of cathode slurry of lithium-ion battery under effects of both poly vinylidene fluoride/carbon black ratio and mixing time

This paper presents the effects of both poly vinylidene fluoride(PVDF)/carbon black(CB)ratio(m PVDF:m CB)and mixing time t on the dispersion mechanism of the cathode slurry of lithium-ion battery(LIB).The dispersion mechanism is deduced from the electrochemical,morphological and rheological properties of the cathode slurry by using electrical impedance spectroscopy(EIS),scanning electron microscopy and rheology methods,respectively.From the perspective of EIS method,static simulation models are established in the COMSOL Multiphysics software;meanwhile,the simulated results are used to verify the correctness of the electrochemical properties of the cathode slurry.As a result,the following conclusions are able to be obtained.Firstly,in the case of the mass ratio m_(PVDF):m_(CB)=5:10,LiCoO_(2) particles are completely coated by the mixture of CB and PVDF to form a stable polymer gel structure.Higher or lower m_(PVDF):m_(CB) leads to the larger impedance and worse dispersion status for the cathode slurry.Secondly,when t=6 min,a good gel-like conductive network structure is formed by coating the thinner evenly dispersed CB–PVDF double layer around LiCoO_(2) particles.Finally,a strategy regarding to both m_(PVDF):m_(CB) and t in experimental scale is proposed,which has the capability of improving the performance of LIB.

Optimization of mixing speed and time to disperse the composite conductive agent composed of carbon black and graphene in lithium-ion battery slurry

This paper proposed an optimal approach to disperse the composite conductive agent which is composed of carbon black(CB)and graphene(Gr)within lithium-ion battery(LIB)slurry with different mixing speeds and mixing times.The internal structures of LIB slurry are characterized by Electrochemical Impedance Spectroscopy,Scanning Electron Microscopy,and Raman experiment.Initially,a composite conductive solution is prepared by mixing the composite conductive agent with NMP solvent under the conditions of five different mixing speeds n_(1)(n_(1)=1000,1100,1200,1300,1400 rpm)in the case of mixing time t_(1)=10 min.Subsequently,LIB slurry is prepared by blending the composite conductive solution,LiCoO_(2)and PVDF-NMP solution under the conditions of five different mixing speeds n_(2)(n_(2)=1000±280,1100±280,1200±280,1300±280,1400±280 rpm)in the case of mixing time t_(2)=6 min.By analyzing the internal structure of different LIB slurries,it shows that in the case of n_(1)=n_(2)=1200 rpm,a conductive network structure is well formed within LIB slurry.Additionally,in order to determine the optimal time to prepare the composite conductive solution for LIB slurry,nine different t_(1)(t_(1)=0,10,20,30,40,50,60,70,80 min)are selected.By analyzing the internal structure of different LIB slurries,a well-formed conductive network structure and a uniformly distributed composite conductive agent are deduced in LIB slurry when t_(1)=50 min.Therefore,it can be concluded that the composite conductive agent composed of CB and Gr is able to be uniformly dispersed in LIB slurry by establishing a well-formed conductive network structure under the optimal mixing speed n_(1)=n_(2)=1200 rpm and the optimal mixing time t_(1)=50 min,t_(2)=6 min.This kind of the internal structure has the potential to be used to further analyze the dispersion characterizations of LIB slurry under different composite conductive agent and CB/Gr ratios with the aim of improving the final performance of LIB.

Caspase-1 Cleavage of Aldolase B Impairs Hepatic Fructose Metabolism and Aggravates Chronic Viral Hepatitis

Of dietary monosaccharides,fructose is primarily metabolized by aldolase B(ALDOB)in the liver,whereas glucose is metabolized elsewhere in the body.It has been documented that overconsumption of dietary fructose,especially industrial fructose,associates significantly with advanced inflammation in chronic hepatitis C(CHC)patients.However,little is known about whether impaired fructolysis might attribute to CHC hepatopathogenesis.Herein we found that the level of ALDOB protein was significantly reduced in CHC patients and mice that were persistently infected by hepatitis C virus(HCV).In vitro,HCV infection activated caspase-1,and caspase-3 to a lesser extent,which proteolyzed ALDOB and blocked fructose metabolism in hepatocytes.Downregulation of ALDOB attenuated HCV replication,indicating an intrinsic anti-HCV role for homeostatic fructolysis.On the other hand,reduced ALDOB caused intracellular fructose 1-phosphate accumulation that provoked severe cellular toxicity through intracellular ATP depletion and heightened glycation,which was aggravated by HCV infection.Taken together,these results have unveiled that inflammatory activation of caspase-1 impairs homeostatic fructolysis and exacerbates liver damage.

Clarification of the dispersion mechanism of three typical chemical dispersants in lithium-ion battery (LIB) slurry

This paper mainly clarified the dispersion mechanism of three typical chemical dispersants which are polyethylene glycol octylphenyl ether(Triton X-100,T-100),polyethylene pyrrolidone(PVP)and carboxymethyl cellulose(CMC)within lithium-ion battery(LIB)slurry.Initially,the optimum amounts of T-100,PVP and CMC are selected from 0%,0.5%,1.5%and 2.5%by evaluating the impedance of LIB slurry in the case of adding each typical chemical dispersant with EIS method.Moreover,the impedance spectrum of three different slurry samples which are PVDF-NMP solution,LiCoO_(2) slurry and Carbon Black(CB)slurry with the optimum amount of each dispersant are also investigated.After using SEM and C element distribution images of LIB slurry to verify the correctness of the dispersion mechanism of each dispersant,it is concluded that the dispersion CMC with its optimum amount 1.5%is the best one to promote the formation of conductive paths and CB-coated LiCoO_(2) network structure within LIB slurry,which has the considerably potential to improve the performance of LIB.

AlphaMat: a material informatics hub connecting data, features, models and applications

The development of modern civil industry,energy and information technology is inseparable from the rapid explorations of new materials.However,only a small fraction of materials being experimentally/computationally studied in a vast chemical space.Artificial intelligence(AI)is promising to address this gap,but faces many challenges,such as data scarcity and inaccurate material descriptors.Here,we develop an AI platform,AlphaMat,that can complete data preprocessing and downstream AI models.With high efficiency and accuracy,AlphaMat exhibits strong powers to model typical 12 material attributes(formation energy,band gap,ionic conductivity,magnetism,bulk modulus,etc.).AlphaMat’s capabilities are further demonstrated to discover thousands of new materials for use in specific domains.AlphaMat does not require users to have strong programming experience,and its effective use will facilitate the development of materials informatics,which is of great significance for the implementation of AI for Science(AI4S).

An end-to-end artificial intelligence platform enables real-time assessment of superionic conductors

Superionic conductors(SCs)exhibiting low ion migration activation energy(Ea)are critical to the performance of electrochemical energy storage devices such as solid-state batteries and fuel cells.However,it is challenging to obtain Ea experimentally and theoretically,and the artificial intelligence(AI)method is expected to bring a breakthrough in predicting Ea.Here,we proposed an AI platform(named AI-IMAE)to predict the Ea of cation and anion conductors,including Li^(+),Na^(+),Ag^(+),Al^(3+),Mg^(2+),Zn^(2+),Cu^((2)+),F^(−),and O^(2−),which is~105 times faster than traditional methods.The proposed AI-IMAE is based on crystal graph neural network models and achieves a holistic average absolute error of 0.19 eV,a median absolute error of 0.09 eV,and a Pearson coefficient of 0.92.Using AI-IMAE,we rapidly discovered 316 promising SCs as solid-state electrolytes and 129 SCs as cathode materials from 144,595 inorganic compounds.AI-IMAE is expected to completely solve the challenge of time-consuming Ea prediction and blaze a new trail for large-scale studies of SCs with excellent performance.As more experimental and high-precision theoretical data become available,AI-IMAE can train custom models and transfer the existing models to new models through transfer learning to constantly meet more demands.

大豆硬脂酰-ACP Δ^9脱氢酶(GmSAD)基因家族的鉴定及功能分析

硬脂酰-ACPΔ~9脱氢酶(Stearoyl-acyl carrier proteinΔ~9 desaturase,SAD)在质体中催化单不饱和油酸或棕榈油酸的合成,是控制植物细胞饱和脂肪酸与不饱和脂肪酸比例的关键酶。为解析大豆油酸合成积累调控机制,文中对大豆Glycine max GmSAD家族成员进行全基因组鉴定和保守功能域及理化性质等分析。应用qRT-PCR检测GmSAD各成员的时空表达谱,构建表达载体并通过农杆菌介导烟草Nicotiana tabacum瞬时表达和油酸缺陷型酵母Saccharomyces cerevisiae突变株BY4389遗传转化测试GmSAD酶活性和生物学功能。结果表明,大豆基因组含有5个GmSADs家族成员,其编码酶蛋白均具有二铁中心和SAD酶特有的2个保守组氨酸富集基序(EENRHG和DEKRHE),预测其活性酶蛋白为同源二聚体。系统进化分析显示5个GmSAD分成2个亚组,分别与拟南芥AtSSI2和AtSAD6亲缘关系较近。GmSAD各成员在大豆根、茎、叶、花和不同发育时期种子等组织中表达谱差异明显,其中GmSAD5在发育种子中、晚期高量表达,与油脂富集时期相吻合。烟草叶片瞬时表达GmSAD5可使叶片组织中油酸和总油脂含量分别提高5.56%和2.73%,而硬脂酸含量相应降低2.46%。缺陷型酵母遗传转化测试显示,过表达GmSAD5能恢复缺陷酵母合成单不饱和油酸的能力和促进油脂积累。总之,大豆GmSAD5对硬脂酸底物选择性较强,能高效催化单不饱和油酸的生物合成,为大豆种子油酸和总油脂积累机制的研究奠定了基础,也可作为油脂品质遗传改良的优异靶标。

Recent Progress in Understanding PAMP- and Effector-Triggered Immunity against the Rice Blast Fungus Magnaporthe oryzae

Rice blast, caused by the fungal pathogen Magnaporthe oryzae, is one of the most destructive diseases of rice worldwide. The rice-M, oryzae pathosystem has become a model in the study of plant-fungal interactions because of its scientific advancement and economic importance. Recent studies have identified a number of new pathogen- associated molecular patterns （PAMPs） and effectors from the blast fungus that trigger rice immune responses upon perception. Interaction analyses between avirulence effectors and their cognate resistance proteins have provided new insights into the molecular basis of plant-fungal interactions. In this review, we summarize the recent research on the characterization of those genes in both M. oryzae and rice that are important for the PAMP- and effector-triggered immunity recognition and signaling processes. We also discuss future directions for research that will further our understanding of this pathosystem.

A Genome-Wide Scenario of Terpene Pathways in Self-pollinated Artemisia annua

Scenarios of genes to metabolites in Artemisia annua remain uninvestigated. Here, we report the use of an integrated approach combining metabolomics, transcriptomics, and gene function analyses to charac- terize gene-to-terpene and terpene pathway scenarios in a self-pollinating variety of this species. Eightyeight metabolites including 22 sesquiterpenes （e.g., artemisinin）, 26 monoterpenes, two triterpenes, one diterpene and 38 other non-polar metabolites were identified from 14 tissues. These metabolites were differentially produced by leaves and flowers at lower to higher positions. Sequences from cDNA libraries of six tissues were assembled into 18 871 contigs and genome-wide gene expression profiles in tissues were strongly associated with developmental stages and spatial specificities. Sequence mining identified 47 genes that mapped to the artemisinin, non-amorphadiene sesquiterpene, monoterpene, triterpene, 2-C- methyl-D-erythritol 4-phosphate and mevalonate pathways. Pearson correlation analysis resulted in network integration that characterized significant correlations of gene-to-gene expression patterns and gene expression-to-metabolite levels in six tissues simultaneously. More importantly, manipulations of amorpha-4,11-diene synthase gene expression not only affected the activity of this pathway toward artemisinin, artemisinic acid, and arteannuin b but also altered non-amorphadiene sesquiterpene and genome-wide volatile profiles. Such gene-to-terpene landscapes associated with different tissues are fundamental to the metabolic engineering of artemisinin.

Intelligent classification model of surrounding rock of tunnel using drilling and blasting method

Classification of surrounding rock is the cornerstone of tunnel design and construction.The traditional methods are mainly qualitative and manual and require extensive professional knowledge and engineering experience.To minimize the effect of the empirical judgment on the accuracy of surrounding rock classification,it is necessary to reduce human participation.An intelligent classification technique based on information technology and artificial intelligence could overcome these issues.In this regard,using 299 groups of drilling parameters collected automatically using intelligent drill jumbos in tunnels for the Zhengzhou-Wanzhou high-speed railway in China,an intelligent-classification surrounding-rock database is constructed in this study.Based on a machine learning algorithm,an intelligent classification model is then developed,which has an overall accuracy of 91.9%.Finally,using the core of the model,the intelligent classification system for the surrounding rock of drilled and blasted tunnels is integrated,and the system is carried by intelligent jumbos to perform automatic recording and transmission of drilling parameters and intelligent classification of the surrounding rock.This approach provides a foundation for the dynamic design and construction(both conventional and intelligent)of tunnels.

Agile Earth Observation Satellite Scheduling Algorithm for Emergency Tasks Based on Multiple Strategies

During the execution of imaging tasks,satellites are often required to observe natural disasters,local wars,and other emergencies,which regularly interferes with the execution of existing schemes.Thus,rapid satellite scheduling is urgently needed.As a new generation of three degree-of-freedom(roll,pitch,and yaw)satellites,agile earth observation satellites(AEOSs)have longer variable-pitch visible time windows for ground targets and are capable of observing at any time within the time windows.Thus,they are very suitable for emergency tasks.However,current task scheduling models and algorithms ignore the time,storage and energy consumed by pitch.Thus,these cannot make full use of the AEOS capabilities to optimize the scheduling for emergency tasks.In this study,we present a fine scheduling model and algorithm to realize the AEOS scheduling for emergency tasks.First,a novel time window division method is proposed to convert a variable-pitch visible time window to multiple fixed-pitch visible time windows.Second,a model that considers flexible pitch and roll capabilities is designed.Finally,a scheduling algorithm based on merging insertion,direct insertion,shifting insertion,deleting insertion,and reinsertion strategies is proposed to solve conflicting problems quickly.To verify the effectiveness of the algorithm,48 groups of comparative experiments are carried out.The experimental results show that the model and algorithm can improve the emergency task completion efficiency of AEOSs and reduce the disturbance measure of the scheme.Furthermore,the proposed method can support hybrid satellite resource scheduling for emergency tasks.