Na_(3)V_(2)(PO_(4))_(3)(NVP)has garnered great attentions as a prospective cathode material for sodium-ion batteries(SIBs)by virtue of its decent theoretical capacity,superior ion conductivity and high structural stab...Na_(3)V_(2)(PO_(4))_(3)(NVP)has garnered great attentions as a prospective cathode material for sodium-ion batteries(SIBs)by virtue of its decent theoretical capacity,superior ion conductivity and high structural stability.However,the inherently poor electronic conductivity and sluggish sodium-ion diffusion kinetics of NVP material give rise to inferior rate performance and unsatisfactory energy density,which strictly confine its further application in SIBs.Thus,it is of significance to boost the sodium storage performance of NVP cathode material.Up to now,many methods have been developed to optimize the electrochemical performance of NVP cathode material.In this review,the latest advances in optimization strategies for improving the electrochemical performance of NVP cathode material are well summarized and discussed,including carbon coating or modification,foreign-ion doping or substitution and nanostructure and morphology design.The foreign-ion doping or substitution is highlighted,involving Na,V,and PO_(4)^(3−)sites,which include single-site doping,multiple-site doping,single-ion doping,multiple-ion doping and so on.Furthermore,the challenges and prospects of high-performance NVP cathode material are also put forward.It is believed that this review can provide a useful reference for designing and developing high-performance NVP cathode material toward the large-scale application in SIBs.展开更多
Machine learning(ML)has strong potential for soil settlement prediction,but determining hyperparameters for ML models is often intricate and laborious.Therefore,we apply Bayesian optimization to determine the optimal ...Machine learning(ML)has strong potential for soil settlement prediction,but determining hyperparameters for ML models is often intricate and laborious.Therefore,we apply Bayesian optimization to determine the optimal hyperparameter combinations,enhancing the effectiveness of ML models for soil parameter inversion.The ML models are trained using numerical simulation data generated with the modified Cam-Clay(MCC)model in ABAQUS software,and their performance is evaluated using ground settlement monitoring data from an airport runway.Five optimized ML models—decision tree(DT),random forest(RF),support vector regression(SVR),deep neural network(DNN),and one-dimensional convolutional neural network(1D-CNN)—are compared in terms of their accuracy for soil parameter inversion and settlement prediction.The results indicate that Bayesian optimization efficiently utilizes prior knowledge to identify the optimal hyperparameters,significantly improving model performance.Among the evaluated models,the 1D-CNN achieves the highest accuracy in soil parameter inversion,generating settlement predictions that closely match real monitoring data.These findings demonstrate the effectiveness of the proposed approach for soil parameter inversion and settlement prediction,and reveal how Bayesian optimization can refine the model selection process.展开更多
Idiopathic pulmonary fibrosis(IPF)is a progressive lung disease and its incidence rate is rapidly rising.However,effective therapies for the treatment of IPF are still lacking.Phosphodiesterase 4(PDE4)inhibitors were ...Idiopathic pulmonary fibrosis(IPF)is a progressive lung disease and its incidence rate is rapidly rising.However,effective therapies for the treatment of IPF are still lacking.Phosphodiesterase 4(PDE4)inhibitors were reported to be potential anti-fibrotic agents.Herein,structure-based hit-to-lead optimization of natural isoaurostatin(8.98μmol/L)resulted in several potent inhibitors of PDE4 with half maximal inhibitory concentration(IC_(50))values ranging from 35 nmol/L to 126 nmol/L.Co-crystal structures revealed that isoaurostatin compounds exhibited different binding patterns from the classic PDE4 inhibitor rolipram and the analogues would favor to be Z configurations other than the corresponding E isomers.Finally,lead 2–9 showed remarkable in vitro/in vivo anti-fibrotic effects indicating its potential as a novel anti-IPF agent.展开更多
Thediagnosis of Dry EyeDisease(DED),however,usually depends on clinical information and complex,high-dimensional datasets.To improve the performance of classification models,this paper proposes a Computer Aided Design...Thediagnosis of Dry EyeDisease(DED),however,usually depends on clinical information and complex,high-dimensional datasets.To improve the performance of classification models,this paper proposes a Computer Aided Design(CAD)system that presents a new method for DED classification called(IAOO-PSO),which is a powerful Feature Selection technique(FS)that integrates with Opposition-Based Learning(OBL)and Particle Swarm Optimization(PSO).We improve the speed of convergence with the PSO algorithmand the exploration with the IAOO algorithm.The IAOO is demonstrated to possess superior global optimization capabilities,as validated on the IEEE Congress on Evolutionary Computation 2022(CEC’22)benchmark suite and compared with seven Metaheuristic(MH)algorithms.Additionally,an IAOO-PSO model based on Support Vector Machines(SVMs)classifier is proposed for FS and classification,where the IAOO-PSO is used to identify the most relevant features.This model was applied to the DED dataset comprising 20,000 cases and 26 features,achieving a high classification accuracy of 99.8%,which significantly outperforms other optimization algorithms.The experimental results demonstrate the reliability,success,and efficiency of the IAOO-PSO technique for both FS and classification in the detection of DED.展开更多
The method for optimizing the hydraulic fracturing parameters of the cube development infill well pad was proposed,aiming at the well pattern characteristic of“multi-layer and multi-period”of the infill wells in Sic...The method for optimizing the hydraulic fracturing parameters of the cube development infill well pad was proposed,aiming at the well pattern characteristic of“multi-layer and multi-period”of the infill wells in Sichuan Basin.The fracture propagation and inter-well interference model were established based on the evolution of 4D in-situ stress,and the evolution characteristics of stress and the mechanism of interference between wells were analyzed.The research shows that the increase in horizontal stress difference and the existence of natural fractures/faults are the main reasons for inter-well interference.Inter-well interference is likely to occur near the fracture zones and between the infill wells and parent wells that have been in production for a long time.When communication channels are formed between the infill wells and parent wells,it can increase the productivity of parent wells in the short term.However,it will have a delayed negative impact on the long-term sustained production of both infill wells and parent wells.The change trend of in-situ stress caused by parent well production is basically consistent with the decline trend of pore pressure.The lateral disturbance range of in-situ stress is initially the same as the fracture length and reaches 1.5 to 1.6 times that length after 2.5 years.The key to avoiding inter-well interference is to optimize the fracturing parameters.By adopting the M-shaped well pattern,the optimal well spacing for the infill wells is 300 m,the cluster spacing is 10 m,and the liquid volume per stage is 1800 m^(3).展开更多
A356alloy was used as the base metal to produce boron carbide(B4C)/A356composites using friction stir processing(FSP).The microstructural and mechanical properties of B4C/A356composites were optimized using artificial...A356alloy was used as the base metal to produce boron carbide(B4C)/A356composites using friction stir processing(FSP).The microstructural and mechanical properties of B4C/A356composites were optimized using artificial neural network(ANN)and non-dominated sorting genetic algorithm-II(NSGA-II).Firstly,microstructural properties of the composites fabricated in different processing conditions were investigated.Results show that FSP parameters such as rotational speed,traverse speed and tool pin profile significantly affect the size of the primary silicon(Si)particles of the base metal,as well as the dispersion quality and volume fraction of reinforcing B4C particles in the composite layer.Higher rotational to traverse speeds ratio accompanied by threaded pin profile leads to better particles distribution,finer Si particles and smaller B4C agglomerations.Secondly,hardness and tensile tests were performed to study mechanical properties of the composites.FSP changes the fracture mechanism from brittle form in the as-received metal to very ductile form in the FSPed specimens.Then,a relation between the FSP parameters and microstructural and mechanical properties of the composites was established using ANN.A modified NSGA-II by incorporating diversity preserving mechanism called theεelimination algorithm was employed to obtain the Pareto-optimal set of FSP parameters.展开更多
Cutting parameters were evaluated and optimized based on multiple performance characteristics including tool wear and size error of drilled hole. Taguchi＇s L27, 3-level, 4-factor orthogonal array was used for the tes...Cutting parameters were evaluated and optimized based on multiple performance characteristics including tool wear and size error of drilled hole. Taguchi＇s L27, 3-level, 4-factor orthogonal array was used for the tests. It is shown that generally abrasive wear and built up edge （BUE） formation were seen in the tool wear, and the comer wear was also of major importance. Flank wear of the cutting tool was found to be mostly dependent upon particle mass fraction, followed by feed rate, drill hardness and spindle speed, respectively. Among the tools used, TiAlN coated carbide drills showed the best performance with regard to the tool wear as well as hole size. Grey relational analysis indicated that drill material was the more influential parameter than feed rate and spindle speed. The results revealed that optimal combination of the drilling parameters could be used to obtain both minimum tool wear and diametral error.展开更多
Motor drives form an essential part of the electric compressors,pumps,braking and actuation systems in the More-Electric Aircraft(MEA).In this paper,the application of Machine Learning(ML)in motor-drive design and opt...Motor drives form an essential part of the electric compressors,pumps,braking and actuation systems in the More-Electric Aircraft(MEA).In this paper,the application of Machine Learning(ML)in motor-drive design and optimization process is investigated.The general idea of using ML is to train surrogate models for the optimization.This training process is based on sample data collected from detailed simulation or experiment of motor drives.However,the Surrogate Role(SR)of ML may vary for different applications.This paper first introduces the principles of ML and then proposes two SRs(direct mapping approach and correction approach)of the ML in a motor-drive optimization process.Two different cases are given for the method comparison and validation of ML SRs.The first case is using the sample data from experiments to train the ML surrogate models.For the second case,the joint-simulation data is utilized for a multi-objective motor-drive optimization problem.It is found that both surrogate roles of ML can provide a good mapping model for the cases and in the second case,three feasible design schemes of ML are proposed and validated for the two SRs.Regarding the time consumption in optimizaiton,the proposed ML models can give one motor-drive design point up to 0.044 s while it takes more than 1.5 mins for the used simulation-based models.展开更多
Real-time monitoring of the 14-MeV D-T fusion neutron yield is urgently required for the triton burnup study on the Experimental Advanced Superconducting Tokamak (EAST). In this study, we developed an optimal design o...Real-time monitoring of the 14-MeV D-T fusion neutron yield is urgently required for the triton burnup study on the Experimental Advanced Superconducting Tokamak (EAST). In this study, we developed an optimal design of a fast-neutron detector based on the scintillating fiber (Sci-Fi) to provide D-T neutron yield through Geant4simulation. The effect on the detection performance is concerned when changing the number of the Sci-Fis embedded in the probe head, minimum distance between the fibers, length of the fibers, or substrate material of the probe head. The maximum number of scintillation photons generated by the n/γ source particles and output by the light guide within an event (event:the entire simulation process for one source particle) was used to quantify the n/γ resolution of the detector as the main basis. And the intrinsic detection efficiency was used as another evaluation criterion. The results demonstrate that the optimal design scheme is to use a 5 cm probe head whose substrate material is pure aluminum, in which 463 Sci-Fis with the same length of 5 cm are embedded, and the minimum distance between the centers of the two fibers is 2 mm. The optimized detector exhibits clear directionality in the simulation, which is in line with the expectation and experimental data provided in the literature. This study presents the variation trends of the performance of the SciFi detector when its main parameters change, which is beneficial for the targeted design and optimization of the Sci-Fi detector used in a specific radiation environment.展开更多
The central composite process optimization was performed by response surface methodology technique using a design for the treatment of methyltin mercaptide with modified semi-coke. The semi-coke from the coal industry...The central composite process optimization was performed by response surface methodology technique using a design for the treatment of methyltin mercaptide with modified semi-coke. The semi-coke from the coal industry was suitably modified by treating it with phosphoric acid, with a thermal activation process. The objective of the process optimization is to reduce the chemical oxygen demand （COD） and NH4＋-N in the methyltin mercaptide industrial effluent. The process variables considered for process optimization are the semi-coke dosage, adsorption time and effluent pH. The optimized process conditions are identified to be a semi-coke dosage of 80 g/L, adsorption time of 90 min and a pH value of 8.34. The ANOVA results indicate that the adsorbent dosage and pH are the significant parameters, while the adsorption time is insignificant, possibly owing to the large range of adsorption time chosen. The textural characteristics of modified semi-coke were analyzed using scanning electron microscopy and nitrogen adsorption isotherm. The average BET surface area of modified semi-coke is estimated to be 915 mE/g, with the average pore volume of 0.71 cm3/g and a average pore diameter of 3.09 nm, with micropore volume contributing to 52.36%.展开更多
The industrially important organic compound 1,3-propanediol （1,3-PDO） is mainly used as a building block for the production of various polymers. In the present study, response surface methodology protocol was follow...The industrially important organic compound 1,3-propanediol （1,3-PDO） is mainly used as a building block for the production of various polymers. In the present study, response surface methodology protocol was followed to determine and optimize fermentation conditions for the maximum production of 1,3-PDO using marine-derived Klebsiella pneumoniae HSL4. Four nutritional supplements together with three independent culture conditions were optimized as follows： 29.3 g/L glycerol, 8.0 g/L K2HPO4, 7.6 g/L （NH4）2SO4, 3.0 g/L KH2PO4, pH 7.1, cultivation at 35℃ for 12 h. Under the optimal conditions, a maximum 1,3-PDO concentration of 14.5 g/L, a productivity of 1.21 g/（L＇h） and a conversion of glycerol of 0.49 g/g were obtained. In comparison with the control conditions, fermentation under the optimized conditions achieved an increase of 38.8% in 1,3-PDO concentration, 39.0% in productivity and 25.7% in glycerol conversion in flask. This enhancement trend was further confirmed when the fermentation was conducted in a 5-L fermentor. The optimized fermentation conditions could be an important basis for developing low- cost, large-scale methods for industrial production of 1,3-PDO in the future.展开更多
In order to resolve direction finding problems in the impulse noise,a direction of arrival(DOA)estimation method is proposed.The proposed DOA estimation method can restrain the impulse noise by using infinite norm exp...In order to resolve direction finding problems in the impulse noise,a direction of arrival(DOA)estimation method is proposed.The proposed DOA estimation method can restrain the impulse noise by using infinite norm exponential kernel covariance matrix and obtain excellent performance via the maximumlikelihood(ML)algorithm.In order to obtain the global optimal solutions of this method,a quantum electromagnetic field optimization(QEFO)algorithm is designed.In view of the QEFO algorithm,the proposed method can resolve the difficulties of DOA estimation in the impulse noise.Comparing with some traditional DOA estimation methods,the proposed DOA estimation method shows high superiority and robustness for determining the DOA of independent and coherent sources,which has been verified via the Monte-Carlo experiments of different schemes,especially in the case of snapshot deficiency,low generalized signal to noise ratio(GSNR)and strong impulse noise.Beyond that,the Cramer-Rao bound(CRB)of angle estimation in the impulse noise and the proof of the convergence of the QEFO algorithm are provided in this paper.展开更多
According to the design principle of the central composite experimental,the method of response surface analysis with three factors and three levels was adopted based on one factor test.A second-order quadratic equatio...According to the design principle of the central composite experimental,the method of response surface analysis with three factors and three levels was adopted based on one factor test.A second-order quadratic equation for photocatalysis of Procion Red MX-5B was built.Response surface and contour were graphed with the decoloration rate of Procion Red MX-5B as the response value.Based on the analysis of the response surface plots and their corresponding contour plots,effects of pH value,irradiation time and catalyst loading were explored.By using this new method,the optimum decoloration condition was obtained as follows:pH value,1.3;irradiation time,49.9 min;catalyst loading,0.57 g/L.In the optimization,R-Squared and Adj R-Squared correlation coefficients for quadratic model were evaluated quite satisfactorily as 0.9310 and 0.8620,respectively.Under the optimum conditions established,the performance of 99.47% for color removal was experimentally reached.It was found that all factors considered have an important effect on the decolorization efficiency of Procion Red MX-5B.By the ANOVA analysis and model confirmation the optimal solution obtained using RSM was experimentally validated and credible with preferable instructional ability for experiments.展开更多
In variational methods,coupled parameter optimization(CPO) often needs a long minimization time window(MTW) to fully incorporate observational information,but the optimal MTW somehow depends on the model nonlinearity....In variational methods,coupled parameter optimization(CPO) often needs a long minimization time window(MTW) to fully incorporate observational information,but the optimal MTW somehow depends on the model nonlinearity.The analytical four-dimensional ensemble-variational(A-4DEnVar) considers model nonlinearity well and avoids adjoint model.It can theoretically be applied to CPO.To verify the feasibility and the ability of the A-4DEnVar in CPO,“twin” experiments based on A-4DEnVar CPO are conducted for the first time with the comparison of four-dimensional variational(4D-Var).Two algorithms use the same background error covariance matrix and optimization algorithm to control variates.The experiments are based on a simple coupled oceanatmosphere model,in which the atmospheric part is the highly nonlinear Lorenz-63 model,and the oceanic part is a slab ocean model.The results show that both A-4DEnVar and 4D-Var can effectively reduce the error of state variables through CPO.Besides,two methods produce almost the same results in most cases when the MTW is less than 560 time steps.The results are similar when the MTW is larger than 560 time steps and less than 880 time steps.The largest MTW of 4 D-Var and A-4DEnVar are 1 200 time steps.Moreover,A-4DEnVar is not sensitive to ensemble size when the MTW is less than 720 time steps.A-4DEnVar obtains satisfactory results in the case of highly nonlinear model and long MTW,suggesting that it has the potential to be widely applied to realistic CPO.展开更多
The structure–activity relationship（SAR） study of a 1 2 3 4 4a 9a-hexahydro-1H-xanthene series of selective,human glucocorticoid receptor a（hGRa） antagonists is reported.Compounds were screened using hydroxyapati...The structure–activity relationship（SAR） study of a 1 2 3 4 4a 9a-hexahydro-1H-xanthene series of selective,human glucocorticoid receptor a（hGRa） antagonists is reported.Compounds were screened using hydroxyapatite-based GR binding and MMTV-Luc co-transfection reporter gene assays.Four different regions of the scaffold were modified to assess the effects on hGRa antagonism and related potency.Compound 8d exhibits an 8-fold better bioactivity than the original hit 1a,as well as an improved chemical stability,which make it a promising lead for the subsequent optimization.展开更多
Back-propagation neural network was applied to predict and optimize the synthetic technology of 2-chloro-4,6-dinitroresorcinol. A model was established based on back-propagation neural network using the experimental d...Back-propagation neural network was applied to predict and optimize the synthetic technology of 2-chloro-4,6-dinitroresorcinol. A model was established based on back-propagation neural network using the experimental data of homogeneous design as the training sample set and the technological parameters were optimized by it. The optimal technological parameters are as follows： the reaction time is 4h, the reaction temperature is 80℃, the molar ratio of NaOH to 4,6-dinitro-1,2,3-trichlorobenzene is 5.5：1, the molar ratio of methanol to 4,6-dinitro-1,2,3- trichlorobenzene is 11：1, and the molar ratio of water to 4,6-dinitro-1,2,3-trichlorobenzene is 70：1. Under the optimal conditions, three groups of experiments were performed and the average yield of 2-chloro-4,6-dinitroresorcinol is 96.64%, the absolute error of it with the predicted value is -1.07%.展开更多
In the presented study, the laser butt-welding of Ti 6Al 4V is investigated using 2.2 kw CO2 laser. Ti 6Al 4V alloy has widespread application in various fields of industries including the medical, nuclear and aerospa...In the presented study, the laser butt-welding of Ti 6Al 4V is investigated using 2.2 kw CO2 laser. Ti 6Al 4V alloy has widespread application in various fields of industries including the medical, nuclear and aerospace. In this study, Response Surface Methodology (RSM) is employed to establish the design of experiments and to optimize the bead geometry. The relationships between the input laser-welding parameters (i.e. laser power, welding speed and focal point position) and the process responses (i.e. welded zone width, heat affected zone width, welded zone area, heat affected zone area and penetration depth) are investigated. The multi-response optimizations are used to optimize the welding process. The optimum welding conditions are identified in order to increase the productivity and minimize the total operating cost. The validation results demonstrate that the developed models are accurate with low percentages of error (less than 12.5%).展开更多
基金partly supported by the National Natural Science Foundation of China(Grant No.52272225).
文摘Na_(3)V_(2)(PO_(4))_(3)(NVP)has garnered great attentions as a prospective cathode material for sodium-ion batteries(SIBs)by virtue of its decent theoretical capacity,superior ion conductivity and high structural stability.However,the inherently poor electronic conductivity and sluggish sodium-ion diffusion kinetics of NVP material give rise to inferior rate performance and unsatisfactory energy density,which strictly confine its further application in SIBs.Thus,it is of significance to boost the sodium storage performance of NVP cathode material.Up to now,many methods have been developed to optimize the electrochemical performance of NVP cathode material.In this review,the latest advances in optimization strategies for improving the electrochemical performance of NVP cathode material are well summarized and discussed,including carbon coating or modification,foreign-ion doping or substitution and nanostructure and morphology design.The foreign-ion doping or substitution is highlighted,involving Na,V,and PO_(4)^(3−)sites,which include single-site doping,multiple-site doping,single-ion doping,multiple-ion doping and so on.Furthermore,the challenges and prospects of high-performance NVP cathode material are also put forward.It is believed that this review can provide a useful reference for designing and developing high-performance NVP cathode material toward the large-scale application in SIBs.
基金supported by the National Natural Science Foundation of China(Nos.52378419 and 52478368).
文摘Machine learning(ML)has strong potential for soil settlement prediction,but determining hyperparameters for ML models is often intricate and laborious.Therefore,we apply Bayesian optimization to determine the optimal hyperparameter combinations,enhancing the effectiveness of ML models for soil parameter inversion.The ML models are trained using numerical simulation data generated with the modified Cam-Clay(MCC)model in ABAQUS software,and their performance is evaluated using ground settlement monitoring data from an airport runway.Five optimized ML models—decision tree(DT),random forest(RF),support vector regression(SVR),deep neural network(DNN),and one-dimensional convolutional neural network(1D-CNN)—are compared in terms of their accuracy for soil parameter inversion and settlement prediction.The results indicate that Bayesian optimization efficiently utilizes prior knowledge to identify the optimal hyperparameters,significantly improving model performance.Among the evaluated models,the 1D-CNN achieves the highest accuracy in soil parameter inversion,generating settlement predictions that closely match real monitoring data.These findings demonstrate the effectiveness of the proposed approach for soil parameter inversion and settlement prediction,and reveal how Bayesian optimization can refine the model selection process.
基金supported by the Natural Science Foundation of China(Nos.22277019,82150204,22307031,22377023,22077143,and 82003594)Key Project of Guangdong Natural Science Foundation(No.2016A030311033)+2 种基金Fundamental Research Funds for Hainan University(Nos.KYQD(ZR)-21031,KYQD(ZR)-21108,KYQD(ZR)-23003,and XTCX2022JKA01)Guangdong Provincial Key Laboratory of Construction Foundation(No.2023B1212060022)Science Foundation of Hainan Province(Nos.KJRC2023B10,824YXQN420,and 324MS018)。
文摘Idiopathic pulmonary fibrosis(IPF)is a progressive lung disease and its incidence rate is rapidly rising.However,effective therapies for the treatment of IPF are still lacking.Phosphodiesterase 4(PDE4)inhibitors were reported to be potential anti-fibrotic agents.Herein,structure-based hit-to-lead optimization of natural isoaurostatin(8.98μmol/L)resulted in several potent inhibitors of PDE4 with half maximal inhibitory concentration(IC_(50))values ranging from 35 nmol/L to 126 nmol/L.Co-crystal structures revealed that isoaurostatin compounds exhibited different binding patterns from the classic PDE4 inhibitor rolipram and the analogues would favor to be Z configurations other than the corresponding E isomers.Finally,lead 2–9 showed remarkable in vitro/in vivo anti-fibrotic effects indicating its potential as a novel anti-IPF agent.
文摘Thediagnosis of Dry EyeDisease(DED),however,usually depends on clinical information and complex,high-dimensional datasets.To improve the performance of classification models,this paper proposes a Computer Aided Design(CAD)system that presents a new method for DED classification called(IAOO-PSO),which is a powerful Feature Selection technique(FS)that integrates with Opposition-Based Learning(OBL)and Particle Swarm Optimization(PSO).We improve the speed of convergence with the PSO algorithmand the exploration with the IAOO algorithm.The IAOO is demonstrated to possess superior global optimization capabilities,as validated on the IEEE Congress on Evolutionary Computation 2022(CEC’22)benchmark suite and compared with seven Metaheuristic(MH)algorithms.Additionally,an IAOO-PSO model based on Support Vector Machines(SVMs)classifier is proposed for FS and classification,where the IAOO-PSO is used to identify the most relevant features.This model was applied to the DED dataset comprising 20,000 cases and 26 features,achieving a high classification accuracy of 99.8%,which significantly outperforms other optimization algorithms.The experimental results demonstrate the reliability,success,and efficiency of the IAOO-PSO technique for both FS and classification in the detection of DED.
基金Supported by the General Program of the NATIONAL NATURAL SCIENCE FOUNDATION OF CHINA(52374004)National Key Research and Development Program(2023YFF06141022023YFE0110900)。
文摘The method for optimizing the hydraulic fracturing parameters of the cube development infill well pad was proposed,aiming at the well pattern characteristic of“multi-layer and multi-period”of the infill wells in Sichuan Basin.The fracture propagation and inter-well interference model were established based on the evolution of 4D in-situ stress,and the evolution characteristics of stress and the mechanism of interference between wells were analyzed.The research shows that the increase in horizontal stress difference and the existence of natural fractures/faults are the main reasons for inter-well interference.Inter-well interference is likely to occur near the fracture zones and between the infill wells and parent wells that have been in production for a long time.When communication channels are formed between the infill wells and parent wells,it can increase the productivity of parent wells in the short term.However,it will have a delayed negative impact on the long-term sustained production of both infill wells and parent wells.The change trend of in-situ stress caused by parent well production is basically consistent with the decline trend of pore pressure.The lateral disturbance range of in-situ stress is initially the same as the fracture length and reaches 1.5 to 1.6 times that length after 2.5 years.The key to avoiding inter-well interference is to optimize the fracturing parameters.By adopting the M-shaped well pattern,the optimal well spacing for the infill wells is 300 m,the cluster spacing is 10 m,and the liquid volume per stage is 1800 m^(3).
文摘硫酸软骨素A(chondroitin sulfate A,CSA)是关节软骨修复中不可或缺的重要成分。CSA由软骨素4-O-磺基转移酶-1(chondroitin 4-O-sulfotransferase-1,C4ST-1)催化软骨素中N-乙酰氨基半乳糖胺(N-acetylgalactosamine,GalNAc)4号位羟基磺酸化合成。然而,由于C4ST-1酶活性较低,其催化能力受限,进而阻碍了CSA的工业化生产进程。为此,该研究旨在通过结合重组菌构建和培养基优化提升C4ST-1酶活性。首先在筛选确定了以毕赤酵母GS115为底盘细胞的基础上,进一步优化了以OST1-α分泌信号肽和SUMO Pro 3促溶标签的组合方式进行C4ST-1分泌表达,经摇瓶发酵C4ST-1最高酶活性为1889.2 U/L。鉴于前期研究发现无机盐对C4ST-1酶活性的抑制以及现有培养基成本较高问题,该研究通过优化培养基组分,发现不添加昂贵成分酵母无氨基氮源(yeast nitrogen base without amino acids,YNB)时,C4ST-1酶活性较原培养基提高68.4%。此外,结合碳源、其他氮源以及生物素的筛选与优化使C4ST-1酶活性进一步提高。最终,在5L发酵罐补料分批发酵72 h时,获得最高酶活性为5040.7 U/L。该研究不仅为CSA规模化生产奠定了基础,也将为其他糖胺聚糖(如肝素、硫酸皮肤素)合成所需的磺基转移酶发酵生产提供借鉴。
文摘A356alloy was used as the base metal to produce boron carbide(B4C)/A356composites using friction stir processing(FSP).The microstructural and mechanical properties of B4C/A356composites were optimized using artificial neural network(ANN)and non-dominated sorting genetic algorithm-II(NSGA-II).Firstly,microstructural properties of the composites fabricated in different processing conditions were investigated.Results show that FSP parameters such as rotational speed,traverse speed and tool pin profile significantly affect the size of the primary silicon(Si)particles of the base metal,as well as the dispersion quality and volume fraction of reinforcing B4C particles in the composite layer.Higher rotational to traverse speeds ratio accompanied by threaded pin profile leads to better particles distribution,finer Si particles and smaller B4C agglomerations.Secondly,hardness and tensile tests were performed to study mechanical properties of the composites.FSP changes the fracture mechanism from brittle form in the as-received metal to very ductile form in the FSPed specimens.Then,a relation between the FSP parameters and microstructural and mechanical properties of the composites was established using ANN.A modified NSGA-II by incorporating diversity preserving mechanism called theεelimination algorithm was employed to obtain the Pareto-optimal set of FSP parameters.
文摘Cutting parameters were evaluated and optimized based on multiple performance characteristics including tool wear and size error of drilled hole. Taguchi＇s L27, 3-level, 4-factor orthogonal array was used for the tests. It is shown that generally abrasive wear and built up edge （BUE） formation were seen in the tool wear, and the comer wear was also of major importance. Flank wear of the cutting tool was found to be mostly dependent upon particle mass fraction, followed by feed rate, drill hardness and spindle speed, respectively. Among the tools used, TiAlN coated carbide drills showed the best performance with regard to the tool wear as well as hole size. Grey relational analysis indicated that drill material was the more influential parameter than feed rate and spindle speed. The results revealed that optimal combination of the drilling parameters could be used to obtain both minimum tool wear and diametral error.
基金funding from the Clean Sky 2 Joint Undertaking under the European Union’s Horizon 2020 research and Innovation Programme No.807081。
文摘Motor drives form an essential part of the electric compressors,pumps,braking and actuation systems in the More-Electric Aircraft(MEA).In this paper,the application of Machine Learning(ML)in motor-drive design and optimization process is investigated.The general idea of using ML is to train surrogate models for the optimization.This training process is based on sample data collected from detailed simulation or experiment of motor drives.However,the Surrogate Role(SR)of ML may vary for different applications.This paper first introduces the principles of ML and then proposes two SRs(direct mapping approach and correction approach)of the ML in a motor-drive optimization process.Two different cases are given for the method comparison and validation of ML SRs.The first case is using the sample data from experiments to train the ML surrogate models.For the second case,the joint-simulation data is utilized for a multi-objective motor-drive optimization problem.It is found that both surrogate roles of ML can provide a good mapping model for the cases and in the second case,three feasible design schemes of ML are proposed and validated for the two SRs.Regarding the time consumption in optimizaiton,the proposed ML models can give one motor-drive design point up to 0.044 s while it takes more than 1.5 mins for the used simulation-based models.
基金supported by the Users with Excellence Program of Hefei Science Center CAS(No.2020HSC-UE012)the Comprehensive Research Facility for Fusion Technology Program of China(No.2018-000052-73-01-001228)the Institute of Energy,Hefei Comprehensive National Science Center(No.21KZS205,21KZL401).
文摘Real-time monitoring of the 14-MeV D-T fusion neutron yield is urgently required for the triton burnup study on the Experimental Advanced Superconducting Tokamak (EAST). In this study, we developed an optimal design of a fast-neutron detector based on the scintillating fiber (Sci-Fi) to provide D-T neutron yield through Geant4simulation. The effect on the detection performance is concerned when changing the number of the Sci-Fis embedded in the probe head, minimum distance between the fibers, length of the fibers, or substrate material of the probe head. The maximum number of scintillation photons generated by the n/γ source particles and output by the light guide within an event (event:the entire simulation process for one source particle) was used to quantify the n/γ resolution of the detector as the main basis. And the intrinsic detection efficiency was used as another evaluation criterion. The results demonstrate that the optimal design scheme is to use a 5 cm probe head whose substrate material is pure aluminum, in which 463 Sci-Fis with the same length of 5 cm are embedded, and the minimum distance between the centers of the two fibers is 2 mm. The optimized detector exhibits clear directionality in the simulation, which is in line with the expectation and experimental data provided in the literature. This study presents the variation trends of the performance of the SciFi detector when its main parameters change, which is beneficial for the targeted design and optimization of the Sci-Fi detector used in a specific radiation environment.
基金Projects(5114703,51004059/E041601)supported by the National Natural Science Foundation of China
文摘The central composite process optimization was performed by response surface methodology technique using a design for the treatment of methyltin mercaptide with modified semi-coke. The semi-coke from the coal industry was suitably modified by treating it with phosphoric acid, with a thermal activation process. The objective of the process optimization is to reduce the chemical oxygen demand （COD） and NH4＋-N in the methyltin mercaptide industrial effluent. The process variables considered for process optimization are the semi-coke dosage, adsorption time and effluent pH. The optimized process conditions are identified to be a semi-coke dosage of 80 g/L, adsorption time of 90 min and a pH value of 8.34. The ANOVA results indicate that the adsorbent dosage and pH are the significant parameters, while the adsorption time is insignificant, possibly owing to the large range of adsorption time chosen. The textural characteristics of modified semi-coke were analyzed using scanning electron microscopy and nitrogen adsorption isotherm. The average BET surface area of modified semi-coke is estimated to be 915 mE/g, with the average pore volume of 0.71 cm3/g and a average pore diameter of 3.09 nm, with micropore volume contributing to 52.36%.
基金Supported by the Scientific Research Project of Marine Public Welfare Industry of China(No.201205020-4)the Knowledge Innovation Project of Chinese Academy of Sciences(No.KSCX2-EW-G-12B)the Administration of Ocean and Fisheries of Guangdong Province(No.GD2012-D01-002)
文摘The industrially important organic compound 1,3-propanediol （1,3-PDO） is mainly used as a building block for the production of various polymers. In the present study, response surface methodology protocol was followed to determine and optimize fermentation conditions for the maximum production of 1,3-PDO using marine-derived Klebsiella pneumoniae HSL4. Four nutritional supplements together with three independent culture conditions were optimized as follows： 29.3 g/L glycerol, 8.0 g/L K2HPO4, 7.6 g/L （NH4）2SO4, 3.0 g/L KH2PO4, pH 7.1, cultivation at 35℃ for 12 h. Under the optimal conditions, a maximum 1,3-PDO concentration of 14.5 g/L, a productivity of 1.21 g/（L＇h） and a conversion of glycerol of 0.49 g/g were obtained. In comparison with the control conditions, fermentation under the optimized conditions achieved an increase of 38.8% in 1,3-PDO concentration, 39.0% in productivity and 25.7% in glycerol conversion in flask. This enhancement trend was further confirmed when the fermentation was conducted in a 5-L fermentor. The optimized fermentation conditions could be an important basis for developing low- cost, large-scale methods for industrial production of 1,3-PDO in the future.
基金supported by the National Natural Science Foundation of China(61571149)the Natural Science Foundation of Heilongjiang Province(LH2020F017)+1 种基金the Initiation Fund for Postdoctoral Research in Heilongjiang Province(LBH-Q19098)the Heilongjiang Province Key Laboratory of High Accuracy Satellite Navigation and Marine Application Laboratory(HKL-2020-Y01).
文摘In order to resolve direction finding problems in the impulse noise,a direction of arrival(DOA)estimation method is proposed.The proposed DOA estimation method can restrain the impulse noise by using infinite norm exponential kernel covariance matrix and obtain excellent performance via the maximumlikelihood(ML)algorithm.In order to obtain the global optimal solutions of this method,a quantum electromagnetic field optimization(QEFO)algorithm is designed.In view of the QEFO algorithm,the proposed method can resolve the difficulties of DOA estimation in the impulse noise.Comparing with some traditional DOA estimation methods,the proposed DOA estimation method shows high superiority and robustness for determining the DOA of independent and coherent sources,which has been verified via the Monte-Carlo experiments of different schemes,especially in the case of snapshot deficiency,low generalized signal to noise ratio(GSNR)and strong impulse noise.Beyond that,the Cramer-Rao bound(CRB)of angle estimation in the impulse noise and the proof of the convergence of the QEFO algorithm are provided in this paper.
基金Sponsored by the National Natural Science Foundation of China (Grant No. 51078100)the National Creative Research Groups granted by NSFC(Grant No. 50821002)+1 种基金Excellent Youth Foundation of Heilongjiang Scientific Committee(Grant No. JC2010-03)State Key Laboratory of Urban Water Resource and Environment(Grant No. 2010DX11)
文摘According to the design principle of the central composite experimental,the method of response surface analysis with three factors and three levels was adopted based on one factor test.A second-order quadratic equation for photocatalysis of Procion Red MX-5B was built.Response surface and contour were graphed with the decoloration rate of Procion Red MX-5B as the response value.Based on the analysis of the response surface plots and their corresponding contour plots,effects of pH value,irradiation time and catalyst loading were explored.By using this new method,the optimum decoloration condition was obtained as follows:pH value,1.3;irradiation time,49.9 min;catalyst loading,0.57 g/L.In the optimization,R-Squared and Adj R-Squared correlation coefficients for quadratic model were evaluated quite satisfactorily as 0.9310 and 0.8620,respectively.Under the optimum conditions established,the performance of 99.47% for color removal was experimentally reached.It was found that all factors considered have an important effect on the decolorization efficiency of Procion Red MX-5B.By the ANOVA analysis and model confirmation the optimal solution obtained using RSM was experimentally validated and credible with preferable instructional ability for experiments.
基金The National Key Research and Development Program under contract No.2021YFC3101501the National Natural Science Foundation of China under contract No.41876014。
文摘In variational methods,coupled parameter optimization(CPO) often needs a long minimization time window(MTW) to fully incorporate observational information,but the optimal MTW somehow depends on the model nonlinearity.The analytical four-dimensional ensemble-variational(A-4DEnVar) considers model nonlinearity well and avoids adjoint model.It can theoretically be applied to CPO.To verify the feasibility and the ability of the A-4DEnVar in CPO,“twin” experiments based on A-4DEnVar CPO are conducted for the first time with the comparison of four-dimensional variational(4D-Var).Two algorithms use the same background error covariance matrix and optimization algorithm to control variates.The experiments are based on a simple coupled oceanatmosphere model,in which the atmospheric part is the highly nonlinear Lorenz-63 model,and the oceanic part is a slab ocean model.The results show that both A-4DEnVar and 4D-Var can effectively reduce the error of state variables through CPO.Besides,two methods produce almost the same results in most cases when the MTW is less than 560 time steps.The results are similar when the MTW is larger than 560 time steps and less than 880 time steps.The largest MTW of 4 D-Var and A-4DEnVar are 1 200 time steps.Moreover,A-4DEnVar is not sensitive to ensemble size when the MTW is less than 720 time steps.A-4DEnVar obtains satisfactory results in the case of highly nonlinear model and long MTW,suggesting that it has the potential to be widely applied to realistic CPO.
基金supported in part by grants from the Ministry of Health of China (Nos. 2012ZX09304-011, 2013ZX09401003-005, 2013ZX09507001 and 2013ZX09507002)Shanghai Science and Technology Development Fund (No. 13DZ2290300)Thousand Talents Program in China
文摘The structure–activity relationship（SAR） study of a 1 2 3 4 4a 9a-hexahydro-1H-xanthene series of selective,human glucocorticoid receptor a（hGRa） antagonists is reported.Compounds were screened using hydroxyapatite-based GR binding and MMTV-Luc co-transfection reporter gene assays.Four different regions of the scaffold were modified to assess the effects on hGRa antagonism and related potency.Compound 8d exhibits an 8-fold better bioactivity than the original hit 1a,as well as an improved chemical stability,which make it a promising lead for the subsequent optimization.
文摘Back-propagation neural network was applied to predict and optimize the synthetic technology of 2-chloro-4,6-dinitroresorcinol. A model was established based on back-propagation neural network using the experimental data of homogeneous design as the training sample set and the technological parameters were optimized by it. The optimal technological parameters are as follows： the reaction time is 4h, the reaction temperature is 80℃, the molar ratio of NaOH to 4,6-dinitro-1,2,3-trichlorobenzene is 5.5：1, the molar ratio of methanol to 4,6-dinitro-1,2,3- trichlorobenzene is 11：1, and the molar ratio of water to 4,6-dinitro-1,2,3-trichlorobenzene is 70：1. Under the optimal conditions, three groups of experiments were performed and the average yield of 2-chloro-4,6-dinitroresorcinol is 96.64%, the absolute error of it with the predicted value is -1.07%.
文摘In the presented study, the laser butt-welding of Ti 6Al 4V is investigated using 2.2 kw CO2 laser. Ti 6Al 4V alloy has widespread application in various fields of industries including the medical, nuclear and aerospace. In this study, Response Surface Methodology (RSM) is employed to establish the design of experiments and to optimize the bead geometry. The relationships between the input laser-welding parameters (i.e. laser power, welding speed and focal point position) and the process responses (i.e. welded zone width, heat affected zone width, welded zone area, heat affected zone area and penetration depth) are investigated. The multi-response optimizations are used to optimize the welding process. The optimum welding conditions are identified in order to increase the productivity and minimize the total operating cost. The validation results demonstrate that the developed models are accurate with low percentages of error (less than 12.5%).
