In this paper, the limitations of the single cube D-optimal design scheme is studied, and a double cube D-optimal design scheme is suggested in order to overcome the limitations. For a sort of incomplete cubic polynom...In this paper, the limitations of the single cube D-optimal design scheme is studied, and a double cube D-optimal design scheme is suggested in order to overcome the limitations. For a sort of incomplete cubic polynomials, the test design of the identification is developed with this new scheme, and the comparation with the single cube scheme is also given. This scheme is shown to be perfectly suitable for the optimal identification of the complete cubic polynomials.展开更多
Logistic regression models for binary response problems are present in a wide variety of industrial, biological, social and medical experiments;therefore, optimum designs are a valuable tool for experimenters, leading...Logistic regression models for binary response problems are present in a wide variety of industrial, biological, social and medical experiments;therefore, optimum designs are a valuable tool for experimenters, leading to estimators of parameters with minimum variance. Our interest in this contribution is to provide explicit formulae for the D-optimal designs as a function of the unknown parameters for the logistic model where q is an indicator variable. We have considered an experiment based on the dose-response to a fly insecticide in which males and females respond in different ways, proposed in Atkinson et al. (1995) [1]. To find the D-optimal designs, this problem has been reduced to a canonical form.展开更多
Electromagnetic stir casting process of A357-Si C nanocomposite was discussed using the D-optimal design of experiment(DODOE) method. As the main objective, nine random experiments obtained by DX-7 software were perfo...Electromagnetic stir casting process of A357-Si C nanocomposite was discussed using the D-optimal design of experiment(DODOE) method. As the main objective, nine random experiments obtained by DX-7 software were performed. By this method, A357-Si C nanocomposites with 0.5, 1.0 and 1.5 wt.% Si C were fabricated at three different frequencies(10, 35 and 60 Hz) in the experimental stage. The microstructural evolution was characterized by scanning electron and optical microscopes, and the mechanical properties were investigated using hardness and roomtemperature uniaxial tensile tests. The results showed that the homogeneous distribution of Si C nanoparticles leads to the microstructure evolution from dendritic to non-dendritic form and a reduction of size by 73.9%. Additionally, based on DODOE, F-values of 44.80 and 179.64 were achieved for yield stress(YS) and ultimate tensile strength(UTS), respectively, implying that the model is significant and the variables(Si C fraction and stirring frequency) were appropriately selected. The optimum values of the Si C fraction and stirring frequency were found to be 1.5 wt.% and 60 Hz, respectively. In this case, YS and UTS for A357-Si C nanocomposites were obtained to be 120 and 188 MPa(57.7% and 57.9 % increase compared with those of the as-cast sample), respectively.展开更多
This paper investigates the optimal design problem for the prediction of the individual parameters in hierarchical linear models with heteroscedastic errors.An equivalence theorem is established to characterize D-opti...This paper investigates the optimal design problem for the prediction of the individual parameters in hierarchical linear models with heteroscedastic errors.An equivalence theorem is established to characterize D-optimality of designs for the prediction based on the mean squared error matrix.The admissibility of designs is also considered and a sufficient condition to simplify the design problem is obtained.The results obtained are illustrated in terms of a simple linear model with random slope and heteroscedastic errors.展开更多
This paper considers a linear regression model involving both quantitative and qualitative factors and an m-dimensional response variable y. The main purpose of this paper is to investigate D-optimal designs when the ...This paper considers a linear regression model involving both quantitative and qualitative factors and an m-dimensional response variable y. The main purpose of this paper is to investigate D-optimal designs when the levels of the qualitative factors interact with the levels of the quantitative factors. Under a general covariance structure of the response vector y, here we establish that the determinant of the information matrix of a product design can be separated into two parts corresponding to the two marginal designs. Moreover, it is also proved that D-optimal designs do not depend on the covariance structure if we assume hierarchically ordered system of regression models.展开更多
[Objectives] To study the optimal proportion and formulation process of Jinweng granule,the physicochemical properties of the optimal preparing process was observed. [Methods] Adopting the D-optimal mixture design met...[Objectives] To study the optimal proportion and formulation process of Jinweng granule,the physicochemical properties of the optimal preparing process was observed. [Methods] Adopting the D-optimal mixture design method,selecting the mixing ratio of starch,dextrin,fumei powder and lactose as tested factors,and selecting the most significant factor between hygroscopicity,formability,solubility as the evaluation index,the optimal proportion of filler was examined by system experiments. Granularity,solubility,the angle of repose,and critical relative humidity( CRH) were used to evaluate the optimal proportion and formulation process of Jinweng granule. [Results]The optimal prescription of Jinweng granule is extract∶ starch∶ dextrin∶ lactose∶ fumei powder( 1∶ 0. 5∶ 0. 05∶ 0. 3∶ 0. 15),and the binder was consisted of 1% sodium carboxymethylcellulose( CMC) slurry and 3% starch syrup. The CRH of the optimum formulation process of granule is 72%,and the fluidity,solubility and granularity were qualified. [Conclusions] The process model established by D-optimum mixture design has good predictability,and the granule prepared by the optimal proportion has good repeatability,and the granule proportion and formulation process is stable and reliable.展开更多
An optimized formulation of a sustained release tablet of Gliclazide was developed. The use of Doptimal design with a polynomial statistical model to analyze dissolution data reduced the number of laboratory tests req...An optimized formulation of a sustained release tablet of Gliclazide was developed. The use of Doptimal design with a polynomial statistical model to analyze dissolution data reduced the number of laboratory tests required to obtain an optimal dosage form. The final formulation contained 22 mg of Methocel®E15LV, 16.5 mg Methocel®E15 and 10.0 mg of Dibasic Calcium Phosphate per 30 mg Gliclazide sustained release tablet. Dissolution studies performed on tablets from 5000 tablet test batches released greater than 90 percent of loaded drug in eight hours. Drug release from the optimized tablets followed a pattern more closely similar to zero-order than other mechanisms of drug release tested. Storage of tablets in accelerated and ambient conditions for 6 and 12 months respectively did not alter any of the physico-chemical properties, drug release or the drug release rate compared to initial observations and dissolution data of the prepared tablets. The addition of potassium phosphate and monosodium phosphate to the tablet reduced the effect pH has on Gliclazide dissolution compared to the commercially available product.展开更多
An optimized formulation of capsules containing Lansoprazole enteric-coated pellets using D-Optimal design with a polynomial statistical model were prepared by using Eudragit?L100 as an enteric coated polymer to provi...An optimized formulation of capsules containing Lansoprazole enteric-coated pellets using D-Optimal design with a polynomial statistical model were prepared by using Eudragit?L100 as an enteric coated polymer to provide resistance to simulated gastric acid dissolution in buffer media. D-Optimal experimental design was used to determine the optimal level for three coating layers that were applied to formulate the enteric-coated pellets including a drug loading layer, a sub-coating, and an outer enteric coating. Dissolution studies were performed on the prepared Lansoprazole capsules. Less than 5 percent of Lansoprazole was released in 60 minutes in an acidic dissolution medium (pH 1.2) and greater than 90 percent of active ingredient was released in the next 60 minutes in a buffer dissolution medium (pH 6.8). The Lansoprazole capsules were stable with no observable change in physico-chemical properties in accelerated and normal storage conditions for 6 and 18 months, respectively. The pharmacokinetic parameters Cmax, Tmax, AUC0-t, and AUC0-∞ were determined after administration of the D-Optimal design optimized capsules of LPZ to healthy beagle dogs and were statistically compared to Gastevin? capsules as a reference (KRKA, Slovenia) using the non-compartmental method with the aid of WinNonlin 5.2 software. The analysis of variance showed that the two formulations did not demonstrate bioequivalence using a 90% confidence interval range (80% - 120%) of Cmax, AUC0-t, and AUC0-∞. No significant difference in Tmax was found at the 0.95 significance level using the Wilcoxon signed-rank test. D-Optimal Experimental Design provided definitive direction for an optimal formulation of capsules containing enteric-coated pellets of lansoprazole loaded within the coating of pellets that provided similar bioequivalence to Gastevin.展开更多
Purpose-Interface management is the process of managing communications,responsibilities and coordination of project parties,phases or physical entities which are dependent on one another.Interface management is a cruc...Purpose-Interface management is the process of managing communications,responsibilities and coordination of project parties,phases or physical entities which are dependent on one another.Interface management is a crucial part of managing any construction project-but particularly important for high-speed railway projects that often have several contractual parties and stakeholders,very long project timelines and huge upfront cost overlays.This paper discusses how various project interfaces were managed during the design and construction of the civil engineering infrastructure for the High Speed Two(HS2)project in the United Kingdom.Design/methodology/approach-The paper uses the case study methodology.Key interfaces on the HS2 project are grouped into various categories and the paper discusses how they were managed within the Area North Integrated Project Team(IPT)of the HS2 project made up of contractor Balfour Beatty VINCI(BBV),the Mott MacDonald SYSTRA Design Joint Venture(DJV)and client HS2 Ltd.3 different case studies drawn from across the IPT are used,each of them highlighting different interfaces and how these interfaces were managed.Findings-The paper shows how innovative technical designs and modern methods of construction were used to address some of the unique and peculiar challenges of designing a brand-new railway in the United Kingdom.Addressing the contrasting and often competing requirements of different stakeholders,coupled with challenging physical constraints of the very limited land available for the project and the use of a rarely used Act of Parliament in the delivery of the project required different approach to interface management.Collaboration and proactive stakeholder engagement are necessary for successful interface management on megaprojects.The authors posit that adopting an integrated approach to engineering and construction management is an essential ingredient for the successful delivery of high-speed railway projects.Originality/value-With many high-speed railway projects around the world coming up in the next few years,understanding the context and challenges for each country will help engineering and design managers adopt appropriate approaches for their projects.The lessons learned on the HS2 project are also transferable to other mega infrastructure projects with complex project interfaces.展开更多
Against the backdrop of escalating global climate change and energy crises,the resource utilization of carbon dioxide(CO_(2)),a major greenhouse gas,has become a crucial pathway for achieving carbon peaking and carbon...Against the backdrop of escalating global climate change and energy crises,the resource utilization of carbon dioxide(CO_(2)),a major greenhouse gas,has become a crucial pathway for achieving carbon peaking and carbon neutrality goals.The hydrogenation of CO_(2)to methanol not only enables carbon sequestration and recycling,but also provides a route to produce high value-added fuels and basic chemical feedstocks,holding significant environmental and economic potential.However,this conversion process is thermodynamically and kinetically limited,and traditional catalyst systems(e.g.,Cu/ZnO/Al_(2)O_(3))exhibit inadequate activity,selectivity,and stability under mild conditions.Therefore,the development of novel high-performance catalysts with precisely tunable structures and functionalities is imperative.Metal-organic frameworks(MOFs),as crystalline porous materials with high surface area,tunable pore structures,and diverse metal-ligand compositions,have the great potential in CO_(2)hydrogenation catalysis.Their structural design flexibility allows for the construction of well-dispersed active sites,tailored electronic environments,and enhanced metal-support interactions.This review systematically summarizes the recent advances in MOF-based and MOF-derived catalysts for CO_(2)hydrogenation to methanol,focusing on four design strategies:(1)spatial confinement and in situ construction,(2)defect engineering and ion-exchange,(3)bimetallic synergy and hybrid structure design,and(4)MOF-derived nanomaterial synthesis.These approaches significantly improve CO_(2)conversion and methanol selectivity by optimizing metal dispersion,interfacial structures,and reaction pathways.The reaction mechanism is further explored by focusing on the three main reaction pathways:the formate pathway(HCOO*),the RWGS(Reverse Water Gas Shift reaction)+CO*hydrogenation pathway,and the trans-COOH pathway.In situ spectroscopic studies and density functional theory(DFT)calculations elucidate the formation and transformation of key intermediates,as well as the roles of active sites,metal-support interfaces,oxygen vacancies,and promoters.Additionally,representative catalytic performance data for MOFbased systems are compiled and compared,demonstrating their advantages over traditional catalysts in terms of CO_(2)conversion,methanol selectivity,and space-time yield.Future perspectives for MOF-based CO_(2)hydrogenation catalysts will prioritize two main directions:structural design and mechanistic understanding.The precise construction of active sites through multi-metallic synergy,defect engineering,and interfacial electronic modulation should be made to enhance catalyst selectivity and stability.In addition,advanced in situ characterization techniques combined with theoretical modeling are essential to unravel the detailed reaction mechanisms and intermediate behaviors,thereby guiding rational catalyst design.Moreover,to enable industrial application,challenges related to thermal/hydrothermal stability,catalyst recyclability,and cost-effective large-scale synthesis must be addressed.The development of green,scalable preparation methods and the integration of MOF catalysts into practical reaction systems(e.g.,flow reactors)will be crucial for bridging the gap between laboratory research and commercial deployment.Ultimately,multi-scale structure-performance optimization and catalytic system integration will be vital for accelerating the industrialization of MOF-based CO_(2)-to-methanol technologies.展开更多
As unmanned underwater vehicles (UUVs) are increasingly designed to perform long-duration missions in highly complex and often extreme environments, traditional design methods face significant and growing challenges^(...As unmanned underwater vehicles (UUVs) are increasingly designed to perform long-duration missions in highly complex and often extreme environments, traditional design methods face significant and growing challenges^([1,2]).展开更多
Compact size,high brightness,and wide field of view(FOV)are key requirements for long-wave infrared imagers used in military surveillance or night navigation.However,to meet the imaging requirements of high resolution...Compact size,high brightness,and wide field of view(FOV)are key requirements for long-wave infrared imagers used in military surveillance or night navigation.However,to meet the imaging requirements of high resolution and wide FOV,infrared optical systems often adopt complex optical lens groups,which will increase the size and weight of the optical system.In this paper,a strategy based on wavefront coding(WFC)is proposed to design a compact wide-FOV infrared imager.A cubic phase mask is inserted into the pupil plane of the infrared imager to correct the aberration.The simulated results show that,the WFC infrared imager has good imaging quality in a wide FOV of±16°.In addition,the WFC infrared imager achieves compactness with its 40 mm×40 mm×40 mm size.A fast focal ratio of 1 combined with an entrance pupil diameter of 25 mm ensures brightness.This work is of significance for designing a compact wide-FOV infrared imager.展开更多
In recent years,the use of deep learning to replace traditional numerical methods for electromagnetic propagation has shown tremendous potential in the rapid design of photonic devices.However,most research on deep le...In recent years,the use of deep learning to replace traditional numerical methods for electromagnetic propagation has shown tremendous potential in the rapid design of photonic devices.However,most research on deep learning has focused on single-layer grating couplers,and the accuracy of multi-layer grating couplers has not yet reached a high level.This paper proposes and demonstrates a novel deep learning network-assisted strategy for inverse design.The network model is based on a multi-layer perceptron(MLP)and incorporates convolutional neural networks(CNNs)and transformers.Through the stacking of multiple layers,it achieves a high-precision design for both multi-layer and single-layer raster couplers with various functionalities.The deep learning network exhibits exceptionally high predictive accuracy,with an average absolute error across the full wavelength range of 1300–1700 nm being only 0.17%,and an even lower predictive absolute error below 0.09%at the specific wavelength of 1550 nm.By combining the deep learning network with the genetic algorithm,we can efficiently design grating couplers that perform different functions.Simulation results indicate that the designed single-wavelength grating couplers achieve coupling efficiencies exceeding 80%at central wavelengths of 1550 nm and 1310 nm.The performance of designed dual-wavelength and broadband grating couplers also reaches high industry standards.Furthermore,the network structure and inverse design method are highly scalable and can be applied not only to multi-layer grating couplers but also directly to the prediction and design of single-layer grating couplers,providing a new perspective for the innovative development of photonic devices.展开更多
In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honey...In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honeycomb cells,was created by constructing arrangement matrices to achieve structural lightweight.The machine learning(ML)framework consisted of a neural network(NN)forward regression model for predicting impact resistance and a multi-objective optimization algorithm for generating high-performance designs.The surrogate of the local design space was initially realized by establishing the NN in the small sample dataset,and the active learning strategy was used to continuously extended the local optimal design until the model converged in the global space.The results indicated that the active learning strategy significantly improved the inference capability of the NN model in unknown design domains.By guiding the iteration direction of the optimization algorithm,lightweight designs with high impact resistance were identified.The energy absorption capacity of the optimal design reached 94.98%of the EARE honeycomb,while the initial peak stress and mass decreased by 28.85%and 19.91%,respectively.Furthermore,Shapley Additive Explanations(SHAP)for global explanation of the NN indicated a strong correlation between the arrangement mode of HCS and its impact resistance.By reducing the stiffness of the cells at the top boundary of the structure,the initial impact damage sustained by the structure can be significantly improved.Overall,this study proposed a general lightweight design method for array structures under impact loads,which is beneficial for the widespread application of honeycomb-based protective structures.展开更多
In recent years,there have been fewer missions to detect neutrons in low Earth orbits(LEO),and the data obtained have been extremely limited.Studying the distribution of the neutron energy spectrum in LEO satellites t...In recent years,there have been fewer missions to detect neutrons in low Earth orbits(LEO),and the data obtained have been extremely limited.Studying the distribution of the neutron energy spectrum in LEO satellites through detection can help solve three major scientific problems:the source of particles in the inner radiation belt,information on solar-accelerated particles,and the proportion of neutrons from different sources in near-Earth space.The detection efficiency and accuracy of neutrons are affected by charged and primary particles in the environment and secondary neutrons produced by the spacecraft itself,which has been a hot research topic.The neutron spectrometer developed in this study adopts two combinations of 15 silicon detectors in terms of detector type and arrangement,which are used for neutron detection via the nuclear reaction method and recoil proton method,respectively,in which a 27μm-thick^(6)LiF conversion layer is used for thermal neutron detection up to 0.4 eV and a 300μm-thick high-density polyethylene conversion layer is used for fast-neutron detection up to 14 MeV and below.The design of the detector set can also remove the influence of primary charged particles and secondary neutrons in the detection environment to a certain extent,thereby improving the accuracy of neutron detection.In this study,the neutron spectrometer hardware,firmware,software design,and basic performance of the front-end readout chip SKIROC2A were tested.The readout circuit of each channel baseline ADC code was less than 17;thus,the channel consistency was good.The RMS noise of the channel baseline was only 7.1 mV and exhibited good stability.The maximum number of events that could be processed per second is 75.The overall power consumption was 3 W,the weight was 792 g,and the volume was less than 1 dm^(3).Furthermore,the neutron spectrometer was tested for principle and detection efficiency using various neutron sources,such as ^(241)Am-Be neutron source,2.5 MeV neutron beam,and 14 MeV neutron beam,and the experiments were analyzed with corresponding simulations.The experimental data and simulation results were in good agreement and met the design requirements.The intrinsic detection efficiency of the probes used in the neutron spectrometer was 1.05%for 14 MeV fast neutrons.展开更多
Lithium-sulfur batteries(LSBs)represent a next-generation energy storage technology,but widespread applications are restricted by the shuttle of lithium polysulfides(LiPSs).The rational design of separators has been d...Lithium-sulfur batteries(LSBs)represent a next-generation energy storage technology,but widespread applications are restricted by the shuttle of lithium polysulfides(LiPSs).The rational design of separators has been demonstrated to be one of the most efficient and cost-effective strategies to curb the shuttle effect,and tremendous research progress has been achieved.The efficiency of a separator depends on its interaction with LiPSs,which is governed by the surface energy and binding strength.Despite several review works that have been reported to advance the separators,most of them primarily focus on active material innovation and construction.The most crucial issues of surface binding energy have not been systematically reviewed,limiting the precise design of efficient separators.In this review,fundamentals related to surface energy and binding interactions with LiPSs are comprehensively analyzed and discussed.With surface binding and energy main lines,the advancements in separator engineering strategies are elaborately summarized and discussed.Moreover,techniques for evaluating affinity to LiPSs are thoroughly analyzed to avoid any ambiguities in measurement.Based on the research context,valuable research directions are suggested to construct efficient separators.This work provides guidelines to regulate the surface binding and energy of separators for high-performance LSBs.展开更多
Machine learning(ML)is recognized as a potent tool for the inverse design of environmental functional material,particularly for complex entities like biochar-based catalysts(BCs).Thus,the tailored BCs can have a disti...Machine learning(ML)is recognized as a potent tool for the inverse design of environmental functional material,particularly for complex entities like biochar-based catalysts(BCs).Thus,the tailored BCs can have a distinct ability to trigger the nonradical pathway in advance oxidation processes(AOPs),promising a stable,rapid and selective degradation of persistent contaminants.However,due to the inherent“black box”nature and limitations of input features,results and conclusions derived from ML may not always be intuitively understood or comprehensively validated.To tackle this challenge,we linked the front-point interpretable analysis approaches with back-point density functional theory(DFT)calculations to form a chained learning strategy for deeper sight into the intrinsic activation mechanism of BCs in AOPs.At the front point,we conducted an easy-to-interpret meta-analysis to validate two strategies for enhancing nonradical pathways by increasing oxygen content and specific surface area(SSA),and prepared oxidized biochar(OBC500)and SSA-increased biochar(SBC900)by controlling pyrolysis conditions and modification methods.Subsequently,experimental results showed that OBC500 and SBC900 had distinct dominant degradation pathways for 1O2 generation and electron transfer,respectively.Finally,at the end point,DFT calculations revealed their active sites and degradation mechanisms.This chained learning strategy elucidates fundamental principles for BC inverse design and showcases the exceptional capacity to integrate computational techniques to accelerate catalyst inverse design.展开更多
From an engineering feasibility standpoint, what level of performance metrics can be ultimately achieved when designing a reactor using well-established nuclear fuels and structural materials that have already undergo...From an engineering feasibility standpoint, what level of performance metrics can be ultimately achieved when designing a reactor using well-established nuclear fuels and structural materials that have already undergone irradiation testing? The irradiation capability, which hinges on parameters like neutron flux level, irradiation channels' volume, and fuel cycle duration, is a core indicator for high-flux reactors. We propose a conceptual design of an ultra-high flux fast reactor(UFFR) with strong irradiation capability, which utilizes U-20Pu-10Zr alloy fuel and employs lead-bismuth as the coolant. The maximum neutron flux in the core reaches 1.32×10^(16) cm^(-2)s^(-1), while the average neutron flux in the irradiation channels attains 1.19×10^(16) cm^(-2)s^(-1). The volume of the central irradiation channel exceeds 10000 cm^(3), and the fuel cycle duration is 165 d, placing all its performance indicators among the top in the world. Based on the analyses of reactor physics and thermalhydraulics, it has been demonstrated that all reactivity coefficients are negative and all physical parameters meet the design criteria, ensuring the inherent safety of UFFR. An assessment of the irradiation capability has been carried out based on californium-252(^(252)Cf) production, indicating that the irradiation capability of UFFR surpasses that of the high flux isotope reactor(HFIR). The yield of ^(252)Cf from UFFR is 14.39 times that of HFIR, and its nuclei conversion rate is 3.21 times that of HFIR.展开更多
文摘In this paper, the limitations of the single cube D-optimal design scheme is studied, and a double cube D-optimal design scheme is suggested in order to overcome the limitations. For a sort of incomplete cubic polynomials, the test design of the identification is developed with this new scheme, and the comparation with the single cube scheme is also given. This scheme is shown to be perfectly suitable for the optimal identification of the complete cubic polynomials.
文摘Logistic regression models for binary response problems are present in a wide variety of industrial, biological, social and medical experiments;therefore, optimum designs are a valuable tool for experimenters, leading to estimators of parameters with minimum variance. Our interest in this contribution is to provide explicit formulae for the D-optimal designs as a function of the unknown parameters for the logistic model where q is an indicator variable. We have considered an experiment based on the dose-response to a fly insecticide in which males and females respond in different ways, proposed in Atkinson et al. (1995) [1]. To find the D-optimal designs, this problem has been reduced to a canonical form.
文摘Electromagnetic stir casting process of A357-Si C nanocomposite was discussed using the D-optimal design of experiment(DODOE) method. As the main objective, nine random experiments obtained by DX-7 software were performed. By this method, A357-Si C nanocomposites with 0.5, 1.0 and 1.5 wt.% Si C were fabricated at three different frequencies(10, 35 and 60 Hz) in the experimental stage. The microstructural evolution was characterized by scanning electron and optical microscopes, and the mechanical properties were investigated using hardness and roomtemperature uniaxial tensile tests. The results showed that the homogeneous distribution of Si C nanoparticles leads to the microstructure evolution from dendritic to non-dendritic form and a reduction of size by 73.9%. Additionally, based on DODOE, F-values of 44.80 and 179.64 were achieved for yield stress(YS) and ultimate tensile strength(UTS), respectively, implying that the model is significant and the variables(Si C fraction and stirring frequency) were appropriately selected. The optimum values of the Si C fraction and stirring frequency were found to be 1.5 wt.% and 60 Hz, respectively. In this case, YS and UTS for A357-Si C nanocomposites were obtained to be 120 and 188 MPa(57.7% and 57.9 % increase compared with those of the as-cast sample), respectively.
基金supported by NSFC Grant(11871143,11971318)the Fundamental Research Funds for the Central UniversitiesShanghai Rising-Star Program(No.20QA1407500).
文摘This paper investigates the optimal design problem for the prediction of the individual parameters in hierarchical linear models with heteroscedastic errors.An equivalence theorem is established to characterize D-optimality of designs for the prediction based on the mean squared error matrix.The admissibility of designs is also considered and a sufficient condition to simplify the design problem is obtained.The results obtained are illustrated in terms of a simple linear model with random slope and heteroscedastic errors.
基金supported by the National Natural Science Foundation of China (Nos.11971318, 11871143)the Fundamental Research Funds for the Central Universities (No.2232020D-38)。
文摘This paper considers a linear regression model involving both quantitative and qualitative factors and an m-dimensional response variable y. The main purpose of this paper is to investigate D-optimal designs when the levels of the qualitative factors interact with the levels of the quantitative factors. Under a general covariance structure of the response vector y, here we establish that the determinant of the information matrix of a product design can be separated into two parts corresponding to the two marginal designs. Moreover, it is also proved that D-optimal designs do not depend on the covariance structure if we assume hierarchically ordered system of regression models.
基金Supported by Public Welfare and Industry Special Fund Project of the Ministry of Agriculture(201303040-05)Natural Science Foundation Project of CQCSTC(2013FYF110600)
文摘[Objectives] To study the optimal proportion and formulation process of Jinweng granule,the physicochemical properties of the optimal preparing process was observed. [Methods] Adopting the D-optimal mixture design method,selecting the mixing ratio of starch,dextrin,fumei powder and lactose as tested factors,and selecting the most significant factor between hygroscopicity,formability,solubility as the evaluation index,the optimal proportion of filler was examined by system experiments. Granularity,solubility,the angle of repose,and critical relative humidity( CRH) were used to evaluate the optimal proportion and formulation process of Jinweng granule. [Results]The optimal prescription of Jinweng granule is extract∶ starch∶ dextrin∶ lactose∶ fumei powder( 1∶ 0. 5∶ 0. 05∶ 0. 3∶ 0. 15),and the binder was consisted of 1% sodium carboxymethylcellulose( CMC) slurry and 3% starch syrup. The CRH of the optimum formulation process of granule is 72%,and the fluidity,solubility and granularity were qualified. [Conclusions] The process model established by D-optimum mixture design has good predictability,and the granule prepared by the optimal proportion has good repeatability,and the granule proportion and formulation process is stable and reliable.
文摘An optimized formulation of a sustained release tablet of Gliclazide was developed. The use of Doptimal design with a polynomial statistical model to analyze dissolution data reduced the number of laboratory tests required to obtain an optimal dosage form. The final formulation contained 22 mg of Methocel®E15LV, 16.5 mg Methocel®E15 and 10.0 mg of Dibasic Calcium Phosphate per 30 mg Gliclazide sustained release tablet. Dissolution studies performed on tablets from 5000 tablet test batches released greater than 90 percent of loaded drug in eight hours. Drug release from the optimized tablets followed a pattern more closely similar to zero-order than other mechanisms of drug release tested. Storage of tablets in accelerated and ambient conditions for 6 and 12 months respectively did not alter any of the physico-chemical properties, drug release or the drug release rate compared to initial observations and dissolution data of the prepared tablets. The addition of potassium phosphate and monosodium phosphate to the tablet reduced the effect pH has on Gliclazide dissolution compared to the commercially available product.
文摘An optimized formulation of capsules containing Lansoprazole enteric-coated pellets using D-Optimal design with a polynomial statistical model were prepared by using Eudragit?L100 as an enteric coated polymer to provide resistance to simulated gastric acid dissolution in buffer media. D-Optimal experimental design was used to determine the optimal level for three coating layers that were applied to formulate the enteric-coated pellets including a drug loading layer, a sub-coating, and an outer enteric coating. Dissolution studies were performed on the prepared Lansoprazole capsules. Less than 5 percent of Lansoprazole was released in 60 minutes in an acidic dissolution medium (pH 1.2) and greater than 90 percent of active ingredient was released in the next 60 minutes in a buffer dissolution medium (pH 6.8). The Lansoprazole capsules were stable with no observable change in physico-chemical properties in accelerated and normal storage conditions for 6 and 18 months, respectively. The pharmacokinetic parameters Cmax, Tmax, AUC0-t, and AUC0-∞ were determined after administration of the D-Optimal design optimized capsules of LPZ to healthy beagle dogs and were statistically compared to Gastevin? capsules as a reference (KRKA, Slovenia) using the non-compartmental method with the aid of WinNonlin 5.2 software. The analysis of variance showed that the two formulations did not demonstrate bioequivalence using a 90% confidence interval range (80% - 120%) of Cmax, AUC0-t, and AUC0-∞. No significant difference in Tmax was found at the 0.95 significance level using the Wilcoxon signed-rank test. D-Optimal Experimental Design provided definitive direction for an optimal formulation of capsules containing enteric-coated pellets of lansoprazole loaded within the coating of pellets that provided similar bioequivalence to Gastevin.
文摘Purpose-Interface management is the process of managing communications,responsibilities and coordination of project parties,phases or physical entities which are dependent on one another.Interface management is a crucial part of managing any construction project-but particularly important for high-speed railway projects that often have several contractual parties and stakeholders,very long project timelines and huge upfront cost overlays.This paper discusses how various project interfaces were managed during the design and construction of the civil engineering infrastructure for the High Speed Two(HS2)project in the United Kingdom.Design/methodology/approach-The paper uses the case study methodology.Key interfaces on the HS2 project are grouped into various categories and the paper discusses how they were managed within the Area North Integrated Project Team(IPT)of the HS2 project made up of contractor Balfour Beatty VINCI(BBV),the Mott MacDonald SYSTRA Design Joint Venture(DJV)and client HS2 Ltd.3 different case studies drawn from across the IPT are used,each of them highlighting different interfaces and how these interfaces were managed.Findings-The paper shows how innovative technical designs and modern methods of construction were used to address some of the unique and peculiar challenges of designing a brand-new railway in the United Kingdom.Addressing the contrasting and often competing requirements of different stakeholders,coupled with challenging physical constraints of the very limited land available for the project and the use of a rarely used Act of Parliament in the delivery of the project required different approach to interface management.Collaboration and proactive stakeholder engagement are necessary for successful interface management on megaprojects.The authors posit that adopting an integrated approach to engineering and construction management is an essential ingredient for the successful delivery of high-speed railway projects.Originality/value-With many high-speed railway projects around the world coming up in the next few years,understanding the context and challenges for each country will help engineering and design managers adopt appropriate approaches for their projects.The lessons learned on the HS2 project are also transferable to other mega infrastructure projects with complex project interfaces.
基金Supported by the National Key Research and Development Program of China(2023YFB4104500,2023YFB4104502)the National Natural Science Foundation of China(22138013)the Taishan Scholar Project(ts201712020).
文摘Against the backdrop of escalating global climate change and energy crises,the resource utilization of carbon dioxide(CO_(2)),a major greenhouse gas,has become a crucial pathway for achieving carbon peaking and carbon neutrality goals.The hydrogenation of CO_(2)to methanol not only enables carbon sequestration and recycling,but also provides a route to produce high value-added fuels and basic chemical feedstocks,holding significant environmental and economic potential.However,this conversion process is thermodynamically and kinetically limited,and traditional catalyst systems(e.g.,Cu/ZnO/Al_(2)O_(3))exhibit inadequate activity,selectivity,and stability under mild conditions.Therefore,the development of novel high-performance catalysts with precisely tunable structures and functionalities is imperative.Metal-organic frameworks(MOFs),as crystalline porous materials with high surface area,tunable pore structures,and diverse metal-ligand compositions,have the great potential in CO_(2)hydrogenation catalysis.Their structural design flexibility allows for the construction of well-dispersed active sites,tailored electronic environments,and enhanced metal-support interactions.This review systematically summarizes the recent advances in MOF-based and MOF-derived catalysts for CO_(2)hydrogenation to methanol,focusing on four design strategies:(1)spatial confinement and in situ construction,(2)defect engineering and ion-exchange,(3)bimetallic synergy and hybrid structure design,and(4)MOF-derived nanomaterial synthesis.These approaches significantly improve CO_(2)conversion and methanol selectivity by optimizing metal dispersion,interfacial structures,and reaction pathways.The reaction mechanism is further explored by focusing on the three main reaction pathways:the formate pathway(HCOO*),the RWGS(Reverse Water Gas Shift reaction)+CO*hydrogenation pathway,and the trans-COOH pathway.In situ spectroscopic studies and density functional theory(DFT)calculations elucidate the formation and transformation of key intermediates,as well as the roles of active sites,metal-support interfaces,oxygen vacancies,and promoters.Additionally,representative catalytic performance data for MOFbased systems are compiled and compared,demonstrating their advantages over traditional catalysts in terms of CO_(2)conversion,methanol selectivity,and space-time yield.Future perspectives for MOF-based CO_(2)hydrogenation catalysts will prioritize two main directions:structural design and mechanistic understanding.The precise construction of active sites through multi-metallic synergy,defect engineering,and interfacial electronic modulation should be made to enhance catalyst selectivity and stability.In addition,advanced in situ characterization techniques combined with theoretical modeling are essential to unravel the detailed reaction mechanisms and intermediate behaviors,thereby guiding rational catalyst design.Moreover,to enable industrial application,challenges related to thermal/hydrothermal stability,catalyst recyclability,and cost-effective large-scale synthesis must be addressed.The development of green,scalable preparation methods and the integration of MOF catalysts into practical reaction systems(e.g.,flow reactors)will be crucial for bridging the gap between laboratory research and commercial deployment.Ultimately,multi-scale structure-performance optimization and catalytic system integration will be vital for accelerating the industrialization of MOF-based CO_(2)-to-methanol technologies.
基金supported by the National Natural Science Foundation of China (Grant No.52405033)。
文摘As unmanned underwater vehicles (UUVs) are increasingly designed to perform long-duration missions in highly complex and often extreme environments, traditional design methods face significant and growing challenges^([1,2]).
文摘Compact size,high brightness,and wide field of view(FOV)are key requirements for long-wave infrared imagers used in military surveillance or night navigation.However,to meet the imaging requirements of high resolution and wide FOV,infrared optical systems often adopt complex optical lens groups,which will increase the size and weight of the optical system.In this paper,a strategy based on wavefront coding(WFC)is proposed to design a compact wide-FOV infrared imager.A cubic phase mask is inserted into the pupil plane of the infrared imager to correct the aberration.The simulated results show that,the WFC infrared imager has good imaging quality in a wide FOV of±16°.In addition,the WFC infrared imager achieves compactness with its 40 mm×40 mm×40 mm size.A fast focal ratio of 1 combined with an entrance pupil diameter of 25 mm ensures brightness.This work is of significance for designing a compact wide-FOV infrared imager.
基金sponsored by the National Key Scientific Instrument and Equipment Development Projects of China(Grant No.62027823)the National Natural Science Foun-dation of China(Grant No.61775048).
文摘In recent years,the use of deep learning to replace traditional numerical methods for electromagnetic propagation has shown tremendous potential in the rapid design of photonic devices.However,most research on deep learning has focused on single-layer grating couplers,and the accuracy of multi-layer grating couplers has not yet reached a high level.This paper proposes and demonstrates a novel deep learning network-assisted strategy for inverse design.The network model is based on a multi-layer perceptron(MLP)and incorporates convolutional neural networks(CNNs)and transformers.Through the stacking of multiple layers,it achieves a high-precision design for both multi-layer and single-layer raster couplers with various functionalities.The deep learning network exhibits exceptionally high predictive accuracy,with an average absolute error across the full wavelength range of 1300–1700 nm being only 0.17%,and an even lower predictive absolute error below 0.09%at the specific wavelength of 1550 nm.By combining the deep learning network with the genetic algorithm,we can efficiently design grating couplers that perform different functions.Simulation results indicate that the designed single-wavelength grating couplers achieve coupling efficiencies exceeding 80%at central wavelengths of 1550 nm and 1310 nm.The performance of designed dual-wavelength and broadband grating couplers also reaches high industry standards.Furthermore,the network structure and inverse design method are highly scalable and can be applied not only to multi-layer grating couplers but also directly to the prediction and design of single-layer grating couplers,providing a new perspective for the innovative development of photonic devices.
基金the financial supports from National Key R&D Program for Young Scientists of China(Grant No.2022YFC3080900)National Natural Science Foundation of China(Grant No.52374181)+1 种基金BIT Research and Innovation Promoting Project(Grant No.2024YCXZ017)supported by Science and Technology Innovation Program of Beijing institute of technology under Grant No.2022CX01025。
文摘In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honeycomb cells,was created by constructing arrangement matrices to achieve structural lightweight.The machine learning(ML)framework consisted of a neural network(NN)forward regression model for predicting impact resistance and a multi-objective optimization algorithm for generating high-performance designs.The surrogate of the local design space was initially realized by establishing the NN in the small sample dataset,and the active learning strategy was used to continuously extended the local optimal design until the model converged in the global space.The results indicated that the active learning strategy significantly improved the inference capability of the NN model in unknown design domains.By guiding the iteration direction of the optimization algorithm,lightweight designs with high impact resistance were identified.The energy absorption capacity of the optimal design reached 94.98%of the EARE honeycomb,while the initial peak stress and mass decreased by 28.85%and 19.91%,respectively.Furthermore,Shapley Additive Explanations(SHAP)for global explanation of the NN indicated a strong correlation between the arrangement mode of HCS and its impact resistance.By reducing the stiffness of the cells at the top boundary of the structure,the initial impact damage sustained by the structure can be significantly improved.Overall,this study proposed a general lightweight design method for array structures under impact loads,which is beneficial for the widespread application of honeycomb-based protective structures.
基金supported by the National Natural Science Foundation of China(NSFC)(Nos.42225405 and U2106202)。
文摘In recent years,there have been fewer missions to detect neutrons in low Earth orbits(LEO),and the data obtained have been extremely limited.Studying the distribution of the neutron energy spectrum in LEO satellites through detection can help solve three major scientific problems:the source of particles in the inner radiation belt,information on solar-accelerated particles,and the proportion of neutrons from different sources in near-Earth space.The detection efficiency and accuracy of neutrons are affected by charged and primary particles in the environment and secondary neutrons produced by the spacecraft itself,which has been a hot research topic.The neutron spectrometer developed in this study adopts two combinations of 15 silicon detectors in terms of detector type and arrangement,which are used for neutron detection via the nuclear reaction method and recoil proton method,respectively,in which a 27μm-thick^(6)LiF conversion layer is used for thermal neutron detection up to 0.4 eV and a 300μm-thick high-density polyethylene conversion layer is used for fast-neutron detection up to 14 MeV and below.The design of the detector set can also remove the influence of primary charged particles and secondary neutrons in the detection environment to a certain extent,thereby improving the accuracy of neutron detection.In this study,the neutron spectrometer hardware,firmware,software design,and basic performance of the front-end readout chip SKIROC2A were tested.The readout circuit of each channel baseline ADC code was less than 17;thus,the channel consistency was good.The RMS noise of the channel baseline was only 7.1 mV and exhibited good stability.The maximum number of events that could be processed per second is 75.The overall power consumption was 3 W,the weight was 792 g,and the volume was less than 1 dm^(3).Furthermore,the neutron spectrometer was tested for principle and detection efficiency using various neutron sources,such as ^(241)Am-Be neutron source,2.5 MeV neutron beam,and 14 MeV neutron beam,and the experiments were analyzed with corresponding simulations.The experimental data and simulation results were in good agreement and met the design requirements.The intrinsic detection efficiency of the probes used in the neutron spectrometer was 1.05%for 14 MeV fast neutrons.
基金supported by the National Natural Science Foundation of China (52172228)the Natural Science Foundation of Fujian Province (2024J01475 and 2023J05127)
文摘Lithium-sulfur batteries(LSBs)represent a next-generation energy storage technology,but widespread applications are restricted by the shuttle of lithium polysulfides(LiPSs).The rational design of separators has been demonstrated to be one of the most efficient and cost-effective strategies to curb the shuttle effect,and tremendous research progress has been achieved.The efficiency of a separator depends on its interaction with LiPSs,which is governed by the surface energy and binding strength.Despite several review works that have been reported to advance the separators,most of them primarily focus on active material innovation and construction.The most crucial issues of surface binding energy have not been systematically reviewed,limiting the precise design of efficient separators.In this review,fundamentals related to surface energy and binding interactions with LiPSs are comprehensively analyzed and discussed.With surface binding and energy main lines,the advancements in separator engineering strategies are elaborately summarized and discussed.Moreover,techniques for evaluating affinity to LiPSs are thoroughly analyzed to avoid any ambiguities in measurement.Based on the research context,valuable research directions are suggested to construct efficient separators.This work provides guidelines to regulate the surface binding and energy of separators for high-performance LSBs.
基金supported by Project of National and Local Joint Engineering Research Center for Biomass Energy Development and Utilization(Harbin Institute of Technology,No.2021A004).
文摘Machine learning(ML)is recognized as a potent tool for the inverse design of environmental functional material,particularly for complex entities like biochar-based catalysts(BCs).Thus,the tailored BCs can have a distinct ability to trigger the nonradical pathway in advance oxidation processes(AOPs),promising a stable,rapid and selective degradation of persistent contaminants.However,due to the inherent“black box”nature and limitations of input features,results and conclusions derived from ML may not always be intuitively understood or comprehensively validated.To tackle this challenge,we linked the front-point interpretable analysis approaches with back-point density functional theory(DFT)calculations to form a chained learning strategy for deeper sight into the intrinsic activation mechanism of BCs in AOPs.At the front point,we conducted an easy-to-interpret meta-analysis to validate two strategies for enhancing nonradical pathways by increasing oxygen content and specific surface area(SSA),and prepared oxidized biochar(OBC500)and SSA-increased biochar(SBC900)by controlling pyrolysis conditions and modification methods.Subsequently,experimental results showed that OBC500 and SBC900 had distinct dominant degradation pathways for 1O2 generation and electron transfer,respectively.Finally,at the end point,DFT calculations revealed their active sites and degradation mechanisms.This chained learning strategy elucidates fundamental principles for BC inverse design and showcases the exceptional capacity to integrate computational techniques to accelerate catalyst inverse design.
基金supported by the National Natural Science Foundation of China (Grant No.12575180)the Lingchuang Research Project of China National Nuclear Corporation (CNNC)。
文摘From an engineering feasibility standpoint, what level of performance metrics can be ultimately achieved when designing a reactor using well-established nuclear fuels and structural materials that have already undergone irradiation testing? The irradiation capability, which hinges on parameters like neutron flux level, irradiation channels' volume, and fuel cycle duration, is a core indicator for high-flux reactors. We propose a conceptual design of an ultra-high flux fast reactor(UFFR) with strong irradiation capability, which utilizes U-20Pu-10Zr alloy fuel and employs lead-bismuth as the coolant. The maximum neutron flux in the core reaches 1.32×10^(16) cm^(-2)s^(-1), while the average neutron flux in the irradiation channels attains 1.19×10^(16) cm^(-2)s^(-1). The volume of the central irradiation channel exceeds 10000 cm^(3), and the fuel cycle duration is 165 d, placing all its performance indicators among the top in the world. Based on the analyses of reactor physics and thermalhydraulics, it has been demonstrated that all reactivity coefficients are negative and all physical parameters meet the design criteria, ensuring the inherent safety of UFFR. An assessment of the irradiation capability has been carried out based on californium-252(^(252)Cf) production, indicating that the irradiation capability of UFFR surpasses that of the high flux isotope reactor(HFIR). The yield of ^(252)Cf from UFFR is 14.39 times that of HFIR, and its nuclei conversion rate is 3.21 times that of HFIR.
