This study introduces a hybrid Cuckoo Search-Deep Neural Network(CS-DNN)model for uncertainty quantification and composition optimization of Na_(1/2)Bi_(1/2)TiO_(3)(NBT)-based dielectric energy storage ceramics.Addres...This study introduces a hybrid Cuckoo Search-Deep Neural Network(CS-DNN)model for uncertainty quantification and composition optimization of Na_(1/2)Bi_(1/2)TiO_(3)(NBT)-based dielectric energy storage ceramics.Addressing the limitations of traditional ferroelectric materials—such as hysteresis loss and low breakdown strength under high electric fields—we fabricate(1−x)NBBT8-xBMT solid solutions via chemical modification and systematically investigate their temperature stability and composition-dependent energy storage performance through XRD,SEM,and electrical characterization.The key innovation lies in integrating the CS metaheuristic algorithm with a DNN,overcoming localminima in training and establishing a robust composition-property prediction framework.Our model accurately predicts room-temperature dielectric constant(ε_(r)),maximum dielectric constant(ε_(max)),dielectric loss(tanδ),discharge energy density(W_(rec)),and charge-discharge efficiency(η)from compositional inputs.A Monte Carlo-based uncertainty quantification framework,combined with the 3σ statistical criterion,demonstrates that CSDNN outperforms conventional DNN models in three critical aspects:Higher prediction accuracy(R^(2)=0.9717 vs.0.9382 for ε_(max));Tighter error distribution,satisfying the 99.7% confidence interval under the 3σprinciple;Enhanced robustness,maintaining stable predictions across a 25% composition span in generalization tests.While the model’s generalization is constrained by both the limited experimental dataset(n=45)and the underlying assumptions of MC-based data augmentation,the CS-DNN framework establishes a machine learning-guided paradigm for accelerated discovery of high-temperature dielectric capacitors through its unique capability in quantifying composition-level energy storage uncertainties.展开更多
The prediction of slope stability is considered as one of the critical concerns in geotechnical engineering.Conventional stochastic analysis with spatially variable slopes is time-consuming and highly computation-dema...The prediction of slope stability is considered as one of the critical concerns in geotechnical engineering.Conventional stochastic analysis with spatially variable slopes is time-consuming and highly computation-demanding.To assess the slope stability problems with a more desirable computational effort,many machine learning(ML)algorithms have been proposed.However,most ML-based techniques require that the training data must be in the same feature space and have the same distribution,and the model may need to be rebuilt when the spatial distribution changes.This paper presents a new ML-based algorithm,which combines the principal component analysis(PCA)-based neural network(NN)and transfer learning(TL)techniques(i.e.PCAeNNeTL)to conduct the stability analysis of slopes with different spatial distributions.The Monte Carlo coupled with finite element simulation is first conducted for data acquisition considering the spatial variability of cohesive strength or friction angle of soils from eight slopes with the same geometry.The PCA method is incorporated into the neural network algorithm(i.e.PCA-NN)to increase the computational efficiency by reducing the input variables.It is found that the PCA-NN algorithm performs well in improving the prediction of slope stability for a given slope in terms of the computational accuracy and computational effort when compared with the other two algorithms(i.e.NN and decision trees,DT).Furthermore,the PCAeNNeTL algorithm shows great potential in assessing the stability of slope even with fewer training data.展开更多
Prediction and sensitivity models,to elucidate the response of phytoplankton biomass to environmental factors in Quanzhou Bay,Fujian,China,were developed using a back propagation(BP) network.The environmental indicato...Prediction and sensitivity models,to elucidate the response of phytoplankton biomass to environmental factors in Quanzhou Bay,Fujian,China,were developed using a back propagation(BP) network.The environmental indicators of coastal phytoplankton biomass were determined and monitoring data for the bay from 2008 was used to train,test and build a three-layer BP artificial neural network with multi-input and single-output.Ten water quality parameters were used to forecast phytoplankton biomass(measured as chlorophyll-a concentration).Correlation coefficient between biomass values predicted by the model and those observed was 0.964,whilst the average relative error of the network was-3.46% and average absolute error was 10.53%.The model thus has high level of accuracy and is suitable for analysis of the influence of aquatic environmental factors on phytoplankton biomass.A global sensitivity analysis was performed to determine the influence of different environmental indicators on phytoplankton biomass.Indicators were classified according to the sensitivity of response and its risk degree.The results indicate that the parameters most relevant to phytoplankton biomass are estuary-related and include pH,sea surface temperature,sea surface salinity,chemical oxygen demand and ammonium.展开更多
Accurate 3-D fracture network model for rock mass in dam foundation is of vital importance for stability,grouting and seepage analysis of dam foundation.With the aim of reducing deviation between fracture network mode...Accurate 3-D fracture network model for rock mass in dam foundation is of vital importance for stability,grouting and seepage analysis of dam foundation.With the aim of reducing deviation between fracture network model and measured data,a 3-D fracture network dynamic modeling method based on error analysis was proposed.Firstly,errors of four fracture volume density estimation methods(proposed by ODA,KULATILAKE,MAULDON,and SONG)and that of four fracture size estimation methods(proposed by EINSTEIN,SONG and TONON)were respectively compared,and the optimal methods were determined.Additionally,error index representing the deviation between fracture network model and measured data was established with integrated use of fractal dimension and relative absolute error(RAE).On this basis,the downhill simplex method was used to build the dynamic modeling method,which takes the minimum of error index as objective function and dynamically adjusts the fracture density and size parameters to correct the error index.Finally,the 3-D fracture network model could be obtained which meets the requirements.The proposed method was applied for 3-D fractures simulation in Miao Wei hydropower project in China for feasibility verification and the error index reduced from 2.618 to 0.337.展开更多
The interception probability of a single missile is the basis for combat plan design and weapon performance evaluation,while its influencing factors are complex and mutually coupled.Existing calculation methods have v...The interception probability of a single missile is the basis for combat plan design and weapon performance evaluation,while its influencing factors are complex and mutually coupled.Existing calculation methods have very limited analysis of the influence mechanism of influencing factors,and none of them has analyzed the influence of the guidance law.This paper considers the influencing factors of both the interceptor and the target more comprehensively.Interceptor parameters include speed,guidance law,guidance error,fuze error,and fragment killing ability,while target performance includes speed,maneuverability,and vulnerability.In this paper,an interception model is established,Monte Carlo simulation is carried out,and the influence mechanism of each factor is analyzed based on the model and simulation results.Finally,this paper proposes a classification-regression neural network to quickly estimate the interception probability based on the value of influencing factors.The proposed method reduces the interference of invalid interception data to valid data,so its prediction accuracy is significantly better than that of pure regression neural networks.展开更多
Healthcare collaboration is crucial for reducing medical errors and ensuring patient safety,and effective collaboration is closely linked to visibility.However,research on how floor plans affect visibility among healt...Healthcare collaboration is crucial for reducing medical errors and ensuring patient safety,and effective collaboration is closely linked to visibility.However,research on how floor plans affect visibility among healthcare members(VHM)in inpatient care units is still insufficient,complicating architectural design decisions.This stems from the lack of methods to assess VHM in inpatient care units,where healthcare members'movements continuously alter their relative positions and visual relationships.This study proposes a novel method integrating agent-based simulation and social network analysis to assess VHM.The method first collects behavioral data from healthcare members to execute agent-based simulation.Subsequently,the simulated positions of healthcare members are used to construct visibility networks.Finally,social network analysis is applied to evaluate the VHM.The findings indicate:(1)the“multi-ring+branching”corridor shape exhibits the highest visibility;(2)shared staff stations improve visibility across different healthcare professionals;(3)spatial relationships between nurse stations and assigned patient rooms affect visibility disparities;(4)mobile nursing stations and interconnected support areas enhance visual communication across disparate zones.This research provides the first quantitative analysis of floor plan effects on VHM.The proposed method offers broad applicability for enhancing visibility assessment,improving healthcare collaboration and reducing medical errors.展开更多
The aim of the research was to create a prediction model for winter rapeseed yield.The constructed model enabled to perform simulation on 30 June,in the current year,immediately before harvesting.An artificial neural ...The aim of the research was to create a prediction model for winter rapeseed yield.The constructed model enabled to perform simulation on 30 June,in the current year,immediately before harvesting.An artificial neural network with multilayer perceptron(MLP) topology was used to build the predictive model.The model was created on the basis of meteorological data(air temperature and atmospheric precipitation) and mineral fertilization data.The data were collected in the period 2008–2017 from 291 productive fields located in Poland,in the southern part of the Opole region.The assessment of the forecast quality created on the basis of the neural model has been verified by defining forecast errors using relative approximation error(RAE),root mean square error(RMS),mean absolute error(MAE),and mean absolute percentage error(MAPE) metrics.An important feature of the created predictive model is the ability to forecast the current agrotechnical year based on current weather and fertilizing data.The lowest value of the MAPE error was obtained for a neural network model based on the MLP network of 21:21-13-6-1:1 structure,which was 9.43%.The performed sensitivity analysis of the network examined the factors that have the greatest impact on the yield of winter rape.The highest rank 1 was obtained by an independent variable with the average air temperature from 1 January to 15 April of 2017(designation by the T1-4_CY model).展开更多
Casino games can be classified in two main categories, i.e. skill games and gambling. Notably, the former refers to games whose outcome is affected by the strategies of players, the latter to those games whose outcome...Casino games can be classified in two main categories, i.e. skill games and gambling. Notably, the former refers to games whose outcome is affected by the strategies of players, the latter to those games whose outcome is completely random. For instance, lotteries are easily recognized as pure gambling, while some variants of Poker (e.g. Texas Hold’em) are usually considered as skill games. In both cases, the theory of probability constitutes the mathematical framework for studying their dynamics, despite their classification. Here, it is worth to consider that when games entail the competition between many players, the structure of interactions can acquire a relevant role. For instance, some games as Bingo are not characterized by this kind of interactions, while other games as Poker, show a network structure, i.e. players interact each other and have the opportunity to share or exchange information. In this paper, we analyze the dynamics of a population composed of two species, i.e. strong and weak agents. The former represents expert players, while the latter beginners, i.e. non-expert ones. Here, pair-wise interactions are based on a very simple game, whose outcome is affected by the nature of the involved agents. In doing so, expert agents have a higher probability to succeed when playing with weak agents, while the success probability is equal when two agents of the same kind face each other. Numerical simulations are performed considering a population arranged in different topologies like regular graphs and in scale-free networks. This choice allows to model dynamics that we might observe on online game platforms. Further aspects as the adaptability of agents are taken into account, e.g. the possibility to improve (i.e. to becomean expert). Results show that complex topologies represent a strong opportunity for experts and a risk for both kinds of agents.展开更多
As a disaster prevention measure based on self-assistance and mutual assistance,disaster prevention maps are being created with citizen participation throughout Japan.The process of creating disaster prevention maps i...As a disaster prevention measure based on self-assistance and mutual assistance,disaster prevention maps are being created with citizen participation throughout Japan.The process of creating disaster prevention maps is itself a disaster prevention measure that contributes to raising awareness of disaster prevention by promoting exchange and cooperation within the region.By focusing on relations between road networks and hazardous elements,we developed a system to support disaster prevention map creation that visualizes roads at high risk during a disaster and facilitates the study of evacuation simulations.This system leads to a completed disaster prevention map in three phases.In the first phase,we use a device with GPS logging functions to collect information related to hazardous elements.In the second phase,we use Google Maps(“online map,”below)to visualize roads with high evacuation risk.In the final phase,we perform a regional evaluation through simulations of disaster-time evacuations.In experimental verifications,by conducting usability tests after creating a disaster prevention map in the target area,we evaluated the system in terms of simple operability and visibility.We found that by implementing this series of processes,even users lacking specialized knowledge regarding disaster prevention can intuitively discover evacuation routes while considering the relations between visualized road networks and hazardous elements.These results show that compared with disaster prevention maps having simple site notations using existing WebGIS systems,disaster prevention maps created by residents while inspecting the target area raise awareness of risks present in the immediate vicinity even in normal times and are an effective support system for prompt disaster prevention measures and evacuation drills.展开更多
In the current study,an artificial neural network(ANN)and a numerical reservoir simulation(NRS)technique are used to analyse reservoir performance under waterflooding in the ZH86 block of the Zhaozhouqiao oilfield,Chi...In the current study,an artificial neural network(ANN)and a numerical reservoir simulation(NRS)technique are used to analyse reservoir performance under waterflooding in the ZH86 block of the Zhaozhouqiao oilfield,China.Using five input datasets extracted from the history-matched NRS model,an NRS-ANN hybrid is trained using a trial-and-error approach.NRS-ANN hybrid model#46(which has 5,10,10,6,6,and 1 neurons in the input layer,four hidden layers,and output layer,respectively)is found to produce the minimal root mean square error on the test dataset.On the validation data,the prediction performance of the selected NRS-ANN hybrid model achieves a minimal root mean square error of 0.0274 m^(3)/day and maximal coefficient of determination and coefficient of correlation values of about 0.9999.The correlation between the block liquid production rate(BLPR,m^(3)/day),block water production rate(BWPR,m^(3)/day),block water cut(BWCT,%),block water injection rate(BWIR,m^(3)/day),and block reservoir pressure(BRP,bar)as input variables and the simulated oil production rate(SOPRH)as the output variable is investigated.There is a positive correlation between SOPRH and BLPR,BWIR,and BWCT,and a negative correlation between SOPRH and BRP and BWPR.Segment B of ZH86 block experiences a 3.8%increase in BLPR,while segments A and C show declines of 1.3%and 1.6%,respectively.These variations in the liquid production rate correspond to changes in SOPRH of 4.3%,1.9%,and 9.7%for segments A,B,and C,respectively.The prediction performance of the NRS-ANN hybrid model is compared with that of a simple NRS model.The accuracy of the NRS-ANN hybrid model in predicting oil production is found to be 1125 times that of the NRS model.Based on these results,it is concluded that the proposed NRS-ANN hybrid provides an accurate and useful tool for analysing reservoir performance under the waterflooding oil recovery technique.展开更多
This paper takes the right branch main channel Bridge of Huai River Bridge in Huainan as the engineering background, uses the finite element software Midas and the ANSYS to simulate and analyze the jacking constructio...This paper takes the right branch main channel Bridge of Huai River Bridge in Huainan as the engineering background, uses the finite element software Midas and the ANSYS to simulate and analyze the jacking construction of the bridge, and according to the theoretical calculation, the construction monitoring plan is developed, and the stress and deformation of the key section and part of the structure are monitored. Construction monitoring combined with 3 g network and data acquisition module, monitoring data for the real time measurement, the centralized acquisition and wireless transmission, accomplish on-line real-time monitoring of the bridge construction process, effective control of jacking construction and monitoring. The comparison between theoretical analysis and measured results shows that the simulation results are reasonable, and the construction monitoring scheme based on 3G network and data collection can be used as reference.展开更多
A systematic approach is proposed to the theme of safety,reliability and global quality of complex networks(material and immaterial)by means of special mathematical tools that allow an adequate geometric characterizat...A systematic approach is proposed to the theme of safety,reliability and global quality of complex networks(material and immaterial)by means of special mathematical tools that allow an adequate geometric characterization and study of the operation,even in the presence of multiple obstacles along the path.To that end,applying the theory of graphs to the problem under study and using a special mathematical model based on stochastic geometry,in this article we consider some regular lattices in which it is possible to schematize the elements of the network,with the fundamental cell with six,eight or 2(n+2)obstacles,calculating the probability of Laplace.In this way it is possible to measure the“degree of impedance”exerted by the anomalies along the network by the obstacles examined.The method can be extended to other regular and/or irregular geometric figures,whose union together constitutes the examined network,allowing to optimize the functioning of the complex system considered.展开更多
This paper analyzes the main elements in NS network simulator, makes adetailed view of dataflow management in a link, a node, and an agent, respectively, and introducesthe information described by its trace file. Base...This paper analyzes the main elements in NS network simulator, makes adetailed view of dataflow management in a link, a node, and an agent, respectively, and introducesthe information described by its trace file. Based on the analysis of transportation and treatmentof different packets in NS, a dataflow state machine is proposed with its states exchange triggeringevents and a dataflow analyzer is designed and implemented according to it. As the machine statefunctions, the analyzer can make statistic of total transportation flux of a specified dataflow andoffer a general fluctuation diagram. Finally, a concrete example is used to test its performance.展开更多
The bearing capacity of interfering footings located near the slope face suffers from reduced bearing capacity due to the formation of the curtailed passive zone. Depending upon the position of the footing, their spac...The bearing capacity of interfering footings located near the slope face suffers from reduced bearing capacity due to the formation of the curtailed passive zone. Depending upon the position of the footing, their spacing and steepness of the slope different extents of bearing capacity reduction can be exhibited. A series of finite element investigation has been done with the aid of Plaxis 3 D v AE.01 to elucidate the influence of various geotechnical and geometrical parameters on the ultimate bearing capacity of interfering surface strip footings located at the crest of the natural soil slope. Based on the large database obtained from the numerical simulation, a6-8-1 Artificial Neural Network architecture has been considered for the assessment of the ultimate bearing capacity of interfering strip footings placed on the crest of natural soil slope. Sensitivity analyses have been conducted to establish the relative significance of the contributory parameters, which exhibited that for the stated problem, apart from shear strength parameters, the setback ratio and spacing of footing are the prime contributory parameters.展开更多
Returning to moon has become a top topic recently. Many studies have shown that soft landing is a challenging problem in lunar exploration. The lunar soft landing in this paper begins from a 100 km circular lunar park...Returning to moon has become a top topic recently. Many studies have shown that soft landing is a challenging problem in lunar exploration. The lunar soft landing in this paper begins from a 100 km circular lunar parking orbit. Once the landing area has been selected and it is time to deorbit for landing, a ΔV burn of 19.4 m/s is performed to establish a 100×15 km elliptical orbit. At perilune, the landing jets are ignited, and a propulsive landing is performed. A guidance and control scheme for lunar soft landing is proposed in the paper, which combines optimal theory with nonlinear neuro-control. Basically, an optimal nonlinear control law based on artificial neural network is presented, on the basis of the optimum trajectory from perilune to lunar surface in terms of Pontryagin's maximum principle according to the terminal boundary conditions and performance index. Therefore some optimal control laws can be carried out in the soft landing system due to the nonlinear mapping function of the neural network. The feasibility and validity of the control laws are verified in a simulation experiment.展开更多
Based on the gene-protein-reaction (GPR) model of S. cerevisiae_iND750 and the method of constraint-based analysis, we first calculated the metabolic flux distribution of S. cere-visiae_iND750. Then we calculated the ...Based on the gene-protein-reaction (GPR) model of S. cerevisiae_iND750 and the method of constraint-based analysis, we first calculated the metabolic flux distribution of S. cere-visiae_iND750. Then we calculated the deletion impact of 438 calculable genes, one by one, on the metabolic flux redistribution of S. cere-visiae_iND750. Next we analyzed the correlation between v (describing deletion impact of one gene) and d (connection degree of one gene) and the correlation between v and Vgene (flux sum controlled by one gene), and found that both of them were not of linear relation. Furthermore, we sought out 38 important genes that most greatly affected the metabolic flux distribution, and determined their functional subsystems. We also found that many of these key genes were related to many but not several subsystems. Because the in silico model of S. cere-visiae_iND750 has been tested by many ex-periments, thus is credible, we can conclude that the result we obtained has biological sig-nificance.展开更多
Liquefaction is one of the most destructive phenomena caused by earthquakes,which has been studied in the issues of potential,triggering and hazard analysis.The strain energy approach is a common method to investigate...Liquefaction is one of the most destructive phenomena caused by earthquakes,which has been studied in the issues of potential,triggering and hazard analysis.The strain energy approach is a common method to investigate liquefaction potential.In this study,two Artificial Neural Network(ANN)models were developed to estimate the liquefaction resistance of sandy soil based on the capacity strain energy concept(W)by using laboratory test data.A large database was collected from the literature.One group of the dataset was utilized for validating the process in order to prevent overtraining the presented model.To investigate the complex influence of fine content(FC)on liquefaction resistance,according to previous studies,the second database was arranged by samples with FC of less than 28%and was used to train the second ANN model.Then,two presented ANN models in this study,in addition to four extra available models,were applied to an additional 20 new samples for comparing their results to show the capability and accuracy of the presented models herein.Furthermore,a parametric sensitivity analysis was performed through Monte Carlo Simulation(MCS)to evaluate the effects of parameters and their uncertainties on the liquefaction resistance of soils.According to the results,the developed models provide a higher accuracy prediction performance than the previously publishedmodels.The sensitivity analysis illustrated that the uncertainties of grading parameters significantly affect the liquefaction resistance of soils.展开更多
With the combination of a new theoretical formula, physical simulation experiments, the technology of artificial neural network and database, an intelligent system for the prediction of sheet metal drawing capability ...With the combination of a new theoretical formula, physical simulation experiments, the technology of artificial neural network and database, an intelligent system for the prediction of sheet metal drawing capability is constructed for the first time. A modified criterion for sheet metal drawing capability is proposed in this paper, namely, the Technological Limiting Drawing Ratio, TLDR = f(R, n, s, t, F, μ,r_d,r_p…). Based on the studies of other scholars, a new formula is derived to predict the TLDR in this paper. Then a series of orthogonal physical simulation experiments are designed to investigate the effect of technological parameters on the TLDR, and the results are analyzed in the paper. Then the predicting system is constructed with the combination of the theoretical formula, orthogonal experiments, the technology of artifocial neural network and database. The predicted results show good agreements with experimental data, so it can be used to avoid the blindness in the selection of sheet metal before stamping. The system operates under the Windows operating system, and it supports the mechanism of Client/Server as well as Intranet, so the system has high engineering value.展开更多
The rolling force model is the basis for reasonable selection of rolling equipment and optimization of rolling process,and the establishment of an accurate mathematical model as well as doing the corresponding paramet...The rolling force model is the basis for reasonable selection of rolling equipment and optimization of rolling process,and the establishment of an accurate mathematical model as well as doing the corresponding parameter analysis has been the focus of research in this field for many years.Different modeling methods of the rolling force and their research progress were introduced,the main methods of which are the theoretical analysis,the finite element simulation,the artificial neural network modeling,the hybrid modeling of theory and neural network,as well as the hybrid modeling of finite element and neural network.Meanwhile,the application examples of rolling force models in thickness control,strip crown control,and schedule optimization were presented,and an outlook on the new directions of future development was made,including establishing new type of hybrid models,solving the black box problem,and realizing the multi-objective optimization accounting for some special requirements.展开更多
Prion diseases are associated with the misfolding of the normal helical cellular form of prion protein (PrPC) into the β-sheet-rich scrapie form (PrPSc) and the subsequent aggregation of PrPSc into amyloid fibrils. R...Prion diseases are associated with the misfolding of the normal helical cellular form of prion protein (PrPC) into the β-sheet-rich scrapie form (PrPSc) and the subsequent aggregation of PrPSc into amyloid fibrils. Recent studies demonstrated that a naturally occurring variant V127 of human PrPC is intrinsically resistant to prion conversion and aggregation, and can completely prevent prion diseases. However, the underlying molecular mechanism remains elusive. Herein we perform multiple microsecond molecular dynamics simulations on both wildtype (WT) and V127 variant of human PrPC to understand at atomic level the protective effect of V127 variant. Our simulations show that G127V mutation not only increases the rigidity of the S2–H2 loop between strand-2 (S2) and helix-2 (H2), but also allosterically enhances the stability of the H2 C-terminal region. Interestingly, previous studies reported that animals with rigid S2–H2 loop usually do not develop prion diseases, and the increase in H2 C-terminal stability can prevent misfolding and oligomerization of prion protein. The allosteric paths from G/V127 to H2 C-terminal region are identified using dynamical network analyses. Moreover, community network analyses illustrate that G127V mutation enhances the global correlations and intra-molecular interactions of PrP, thus stabilizing the overall PrPC structure and inhibiting its conversion into PrPSc. This study provides mechanistic understanding of human V127 variant in preventing prion conversion which may be helpful for the rational design of potent anti-prion compounds.展开更多
基金supported by the Postgraduate Education Reform and Quality Improvement Project of Henan Province(Grant Nos.YJS2023JD52 and YJS2025GZZ48)the Zhumadian 2023 Major Science and Technology Special Project(Grant No.ZMD SZDZX2023002)+1 种基金2025 Henan Province International Science and Technology Cooperation Project(Cultivation Project,No.252102521011)Research Merit-Based Funding Program for Overseas Educated Personnel in Henan Province(Letter of Henan Human Resources and Social Security Office[2025]No.37).
文摘This study introduces a hybrid Cuckoo Search-Deep Neural Network(CS-DNN)model for uncertainty quantification and composition optimization of Na_(1/2)Bi_(1/2)TiO_(3)(NBT)-based dielectric energy storage ceramics.Addressing the limitations of traditional ferroelectric materials—such as hysteresis loss and low breakdown strength under high electric fields—we fabricate(1−x)NBBT8-xBMT solid solutions via chemical modification and systematically investigate their temperature stability and composition-dependent energy storage performance through XRD,SEM,and electrical characterization.The key innovation lies in integrating the CS metaheuristic algorithm with a DNN,overcoming localminima in training and establishing a robust composition-property prediction framework.Our model accurately predicts room-temperature dielectric constant(ε_(r)),maximum dielectric constant(ε_(max)),dielectric loss(tanδ),discharge energy density(W_(rec)),and charge-discharge efficiency(η)from compositional inputs.A Monte Carlo-based uncertainty quantification framework,combined with the 3σ statistical criterion,demonstrates that CSDNN outperforms conventional DNN models in three critical aspects:Higher prediction accuracy(R^(2)=0.9717 vs.0.9382 for ε_(max));Tighter error distribution,satisfying the 99.7% confidence interval under the 3σprinciple;Enhanced robustness,maintaining stable predictions across a 25% composition span in generalization tests.While the model’s generalization is constrained by both the limited experimental dataset(n=45)and the underlying assumptions of MC-based data augmentation,the CS-DNN framework establishes a machine learning-guided paradigm for accelerated discovery of high-temperature dielectric capacitors through its unique capability in quantifying composition-level energy storage uncertainties.
基金supported by the National Natural Science Foundation of China(Grant No.52008402)the Central South University autonomous exploration project(Grant No.2021zzts0790).
文摘The prediction of slope stability is considered as one of the critical concerns in geotechnical engineering.Conventional stochastic analysis with spatially variable slopes is time-consuming and highly computation-demanding.To assess the slope stability problems with a more desirable computational effort,many machine learning(ML)algorithms have been proposed.However,most ML-based techniques require that the training data must be in the same feature space and have the same distribution,and the model may need to be rebuilt when the spatial distribution changes.This paper presents a new ML-based algorithm,which combines the principal component analysis(PCA)-based neural network(NN)and transfer learning(TL)techniques(i.e.PCAeNNeTL)to conduct the stability analysis of slopes with different spatial distributions.The Monte Carlo coupled with finite element simulation is first conducted for data acquisition considering the spatial variability of cohesive strength or friction angle of soils from eight slopes with the same geometry.The PCA method is incorporated into the neural network algorithm(i.e.PCA-NN)to increase the computational efficiency by reducing the input variables.It is found that the PCA-NN algorithm performs well in improving the prediction of slope stability for a given slope in terms of the computational accuracy and computational effort when compared with the other two algorithms(i.e.NN and decision trees,DT).Furthermore,the PCAeNNeTL algorithm shows great potential in assessing the stability of slope even with fewer training data.
基金Supported by the Ocean Public Welfare Scientific Research Project,State Oceanic Administration of China(No.200705029)the National Special Fund for Basic Science and Technology of China(No.2012FY112500)the National Non-profit Institute Basic Research Fund(No.FIO2011T06)
文摘Prediction and sensitivity models,to elucidate the response of phytoplankton biomass to environmental factors in Quanzhou Bay,Fujian,China,were developed using a back propagation(BP) network.The environmental indicators of coastal phytoplankton biomass were determined and monitoring data for the bay from 2008 was used to train,test and build a three-layer BP artificial neural network with multi-input and single-output.Ten water quality parameters were used to forecast phytoplankton biomass(measured as chlorophyll-a concentration).Correlation coefficient between biomass values predicted by the model and those observed was 0.964,whilst the average relative error of the network was-3.46% and average absolute error was 10.53%.The model thus has high level of accuracy and is suitable for analysis of the influence of aquatic environmental factors on phytoplankton biomass.A global sensitivity analysis was performed to determine the influence of different environmental indicators on phytoplankton biomass.Indicators were classified according to the sensitivity of response and its risk degree.The results indicate that the parameters most relevant to phytoplankton biomass are estuary-related and include pH,sea surface temperature,sea surface salinity,chemical oxygen demand and ammonium.
基金Project(51321065)supported by the Innovative Research Groups of the National Natural Science Foundation of ChinaProject(2013CB035904)supported by the National Basic Research Program of China(973 Program)Project(51439005)supported by the National Natural Science Foundation of China
文摘Accurate 3-D fracture network model for rock mass in dam foundation is of vital importance for stability,grouting and seepage analysis of dam foundation.With the aim of reducing deviation between fracture network model and measured data,a 3-D fracture network dynamic modeling method based on error analysis was proposed.Firstly,errors of four fracture volume density estimation methods(proposed by ODA,KULATILAKE,MAULDON,and SONG)and that of four fracture size estimation methods(proposed by EINSTEIN,SONG and TONON)were respectively compared,and the optimal methods were determined.Additionally,error index representing the deviation between fracture network model and measured data was established with integrated use of fractal dimension and relative absolute error(RAE).On this basis,the downhill simplex method was used to build the dynamic modeling method,which takes the minimum of error index as objective function and dynamically adjusts the fracture density and size parameters to correct the error index.Finally,the 3-D fracture network model could be obtained which meets the requirements.The proposed method was applied for 3-D fractures simulation in Miao Wei hydropower project in China for feasibility verification and the error index reduced from 2.618 to 0.337.
基金supported by the Foundation Strengthening Program Technology Field Foundation(2020-JCJQ-JJ-132)。
文摘The interception probability of a single missile is the basis for combat plan design and weapon performance evaluation,while its influencing factors are complex and mutually coupled.Existing calculation methods have very limited analysis of the influence mechanism of influencing factors,and none of them has analyzed the influence of the guidance law.This paper considers the influencing factors of both the interceptor and the target more comprehensively.Interceptor parameters include speed,guidance law,guidance error,fuze error,and fragment killing ability,while target performance includes speed,maneuverability,and vulnerability.In this paper,an interception model is established,Monte Carlo simulation is carried out,and the influence mechanism of each factor is analyzed based on the model and simulation results.Finally,this paper proposes a classification-regression neural network to quickly estimate the interception probability based on the value of influencing factors.The proposed method reduces the interference of invalid interception data to valid data,so its prediction accuracy is significantly better than that of pure regression neural networks.
基金funded by the National Natural Science Foundation of China(Grant No.51978143)。
文摘Healthcare collaboration is crucial for reducing medical errors and ensuring patient safety,and effective collaboration is closely linked to visibility.However,research on how floor plans affect visibility among healthcare members(VHM)in inpatient care units is still insufficient,complicating architectural design decisions.This stems from the lack of methods to assess VHM in inpatient care units,where healthcare members'movements continuously alter their relative positions and visual relationships.This study proposes a novel method integrating agent-based simulation and social network analysis to assess VHM.The method first collects behavioral data from healthcare members to execute agent-based simulation.Subsequently,the simulated positions of healthcare members are used to construct visibility networks.Finally,social network analysis is applied to evaluate the VHM.The findings indicate:(1)the“multi-ring+branching”corridor shape exhibits the highest visibility;(2)shared staff stations improve visibility across different healthcare professionals;(3)spatial relationships between nurse stations and assigned patient rooms affect visibility disparities;(4)mobile nursing stations and interconnected support areas enhance visual communication across disparate zones.This research provides the first quantitative analysis of floor plan effects on VHM.The proposed method offers broad applicability for enhancing visibility assessment,improving healthcare collaboration and reducing medical errors.
文摘The aim of the research was to create a prediction model for winter rapeseed yield.The constructed model enabled to perform simulation on 30 June,in the current year,immediately before harvesting.An artificial neural network with multilayer perceptron(MLP) topology was used to build the predictive model.The model was created on the basis of meteorological data(air temperature and atmospheric precipitation) and mineral fertilization data.The data were collected in the period 2008–2017 from 291 productive fields located in Poland,in the southern part of the Opole region.The assessment of the forecast quality created on the basis of the neural model has been verified by defining forecast errors using relative approximation error(RAE),root mean square error(RMS),mean absolute error(MAE),and mean absolute percentage error(MAPE) metrics.An important feature of the created predictive model is the ability to forecast the current agrotechnical year based on current weather and fertilizing data.The lowest value of the MAPE error was obtained for a neural network model based on the MLP network of 21:21-13-6-1:1 structure,which was 9.43%.The performed sensitivity analysis of the network examined the factors that have the greatest impact on the yield of winter rape.The highest rank 1 was obtained by an independent variable with the average air temperature from 1 January to 15 April of 2017(designation by the T1-4_CY model).
文摘Casino games can be classified in two main categories, i.e. skill games and gambling. Notably, the former refers to games whose outcome is affected by the strategies of players, the latter to those games whose outcome is completely random. For instance, lotteries are easily recognized as pure gambling, while some variants of Poker (e.g. Texas Hold’em) are usually considered as skill games. In both cases, the theory of probability constitutes the mathematical framework for studying their dynamics, despite their classification. Here, it is worth to consider that when games entail the competition between many players, the structure of interactions can acquire a relevant role. For instance, some games as Bingo are not characterized by this kind of interactions, while other games as Poker, show a network structure, i.e. players interact each other and have the opportunity to share or exchange information. In this paper, we analyze the dynamics of a population composed of two species, i.e. strong and weak agents. The former represents expert players, while the latter beginners, i.e. non-expert ones. Here, pair-wise interactions are based on a very simple game, whose outcome is affected by the nature of the involved agents. In doing so, expert agents have a higher probability to succeed when playing with weak agents, while the success probability is equal when two agents of the same kind face each other. Numerical simulations are performed considering a population arranged in different topologies like regular graphs and in scale-free networks. This choice allows to model dynamics that we might observe on online game platforms. Further aspects as the adaptability of agents are taken into account, e.g. the possibility to improve (i.e. to becomean expert). Results show that complex topologies represent a strong opportunity for experts and a risk for both kinds of agents.
基金This work was supported by JSPS KAKENHI Grant Number JP20K20122.
文摘As a disaster prevention measure based on self-assistance and mutual assistance,disaster prevention maps are being created with citizen participation throughout Japan.The process of creating disaster prevention maps is itself a disaster prevention measure that contributes to raising awareness of disaster prevention by promoting exchange and cooperation within the region.By focusing on relations between road networks and hazardous elements,we developed a system to support disaster prevention map creation that visualizes roads at high risk during a disaster and facilitates the study of evacuation simulations.This system leads to a completed disaster prevention map in three phases.In the first phase,we use a device with GPS logging functions to collect information related to hazardous elements.In the second phase,we use Google Maps(“online map,”below)to visualize roads with high evacuation risk.In the final phase,we perform a regional evaluation through simulations of disaster-time evacuations.In experimental verifications,by conducting usability tests after creating a disaster prevention map in the target area,we evaluated the system in terms of simple operability and visibility.We found that by implementing this series of processes,even users lacking specialized knowledge regarding disaster prevention can intuitively discover evacuation routes while considering the relations between visualized road networks and hazardous elements.These results show that compared with disaster prevention maps having simple site notations using existing WebGIS systems,disaster prevention maps created by residents while inspecting the target area raise awareness of risks present in the immediate vicinity even in normal times and are an effective support system for prompt disaster prevention measures and evacuation drills.
基金Funding for this research was provided by the Department of Petroleum Engineering,China University of Geosciences,under the sponsorship of the China Scholarship Council (CSC)the National Natural Science Foundation of China through grant nos.41972326 and 51774258.
文摘In the current study,an artificial neural network(ANN)and a numerical reservoir simulation(NRS)technique are used to analyse reservoir performance under waterflooding in the ZH86 block of the Zhaozhouqiao oilfield,China.Using five input datasets extracted from the history-matched NRS model,an NRS-ANN hybrid is trained using a trial-and-error approach.NRS-ANN hybrid model#46(which has 5,10,10,6,6,and 1 neurons in the input layer,four hidden layers,and output layer,respectively)is found to produce the minimal root mean square error on the test dataset.On the validation data,the prediction performance of the selected NRS-ANN hybrid model achieves a minimal root mean square error of 0.0274 m^(3)/day and maximal coefficient of determination and coefficient of correlation values of about 0.9999.The correlation between the block liquid production rate(BLPR,m^(3)/day),block water production rate(BWPR,m^(3)/day),block water cut(BWCT,%),block water injection rate(BWIR,m^(3)/day),and block reservoir pressure(BRP,bar)as input variables and the simulated oil production rate(SOPRH)as the output variable is investigated.There is a positive correlation between SOPRH and BLPR,BWIR,and BWCT,and a negative correlation between SOPRH and BRP and BWPR.Segment B of ZH86 block experiences a 3.8%increase in BLPR,while segments A and C show declines of 1.3%and 1.6%,respectively.These variations in the liquid production rate correspond to changes in SOPRH of 4.3%,1.9%,and 9.7%for segments A,B,and C,respectively.The prediction performance of the NRS-ANN hybrid model is compared with that of a simple NRS model.The accuracy of the NRS-ANN hybrid model in predicting oil production is found to be 1125 times that of the NRS model.Based on these results,it is concluded that the proposed NRS-ANN hybrid provides an accurate and useful tool for analysing reservoir performance under the waterflooding oil recovery technique.
文摘This paper takes the right branch main channel Bridge of Huai River Bridge in Huainan as the engineering background, uses the finite element software Midas and the ANSYS to simulate and analyze the jacking construction of the bridge, and according to the theoretical calculation, the construction monitoring plan is developed, and the stress and deformation of the key section and part of the structure are monitored. Construction monitoring combined with 3 g network and data acquisition module, monitoring data for the real time measurement, the centralized acquisition and wireless transmission, accomplish on-line real-time monitoring of the bridge construction process, effective control of jacking construction and monitoring. The comparison between theoretical analysis and measured results shows that the simulation results are reasonable, and the construction monitoring scheme based on 3G network and data collection can be used as reference.
文摘A systematic approach is proposed to the theme of safety,reliability and global quality of complex networks(material and immaterial)by means of special mathematical tools that allow an adequate geometric characterization and study of the operation,even in the presence of multiple obstacles along the path.To that end,applying the theory of graphs to the problem under study and using a special mathematical model based on stochastic geometry,in this article we consider some regular lattices in which it is possible to schematize the elements of the network,with the fundamental cell with six,eight or 2(n+2)obstacles,calculating the probability of Laplace.In this way it is possible to measure the“degree of impedance”exerted by the anomalies along the network by the obstacles examined.The method can be extended to other regular and/or irregular geometric figures,whose union together constitutes the examined network,allowing to optimize the functioning of the complex system considered.
基金The Natural Science Foundation of Jiangsu Province (BK2001205).
文摘This paper analyzes the main elements in NS network simulator, makes adetailed view of dataflow management in a link, a node, and an agent, respectively, and introducesthe information described by its trace file. Based on the analysis of transportation and treatmentof different packets in NS, a dataflow state machine is proposed with its states exchange triggeringevents and a dataflow analyzer is designed and implemented according to it. As the machine statefunctions, the analyzer can make statistic of total transportation flux of a specified dataflow andoffer a general fluctuation diagram. Finally, a concrete example is used to test its performance.
文摘The bearing capacity of interfering footings located near the slope face suffers from reduced bearing capacity due to the formation of the curtailed passive zone. Depending upon the position of the footing, their spacing and steepness of the slope different extents of bearing capacity reduction can be exhibited. A series of finite element investigation has been done with the aid of Plaxis 3 D v AE.01 to elucidate the influence of various geotechnical and geometrical parameters on the ultimate bearing capacity of interfering surface strip footings located at the crest of the natural soil slope. Based on the large database obtained from the numerical simulation, a6-8-1 Artificial Neural Network architecture has been considered for the assessment of the ultimate bearing capacity of interfering strip footings placed on the crest of natural soil slope. Sensitivity analyses have been conducted to establish the relative significance of the contributory parameters, which exhibited that for the stated problem, apart from shear strength parameters, the setback ratio and spacing of footing are the prime contributory parameters.
文摘Returning to moon has become a top topic recently. Many studies have shown that soft landing is a challenging problem in lunar exploration. The lunar soft landing in this paper begins from a 100 km circular lunar parking orbit. Once the landing area has been selected and it is time to deorbit for landing, a ΔV burn of 19.4 m/s is performed to establish a 100×15 km elliptical orbit. At perilune, the landing jets are ignited, and a propulsive landing is performed. A guidance and control scheme for lunar soft landing is proposed in the paper, which combines optimal theory with nonlinear neuro-control. Basically, an optimal nonlinear control law based on artificial neural network is presented, on the basis of the optimum trajectory from perilune to lunar surface in terms of Pontryagin's maximum principle according to the terminal boundary conditions and performance index. Therefore some optimal control laws can be carried out in the soft landing system due to the nonlinear mapping function of the neural network. The feasibility and validity of the control laws are verified in a simulation experiment.
文摘Based on the gene-protein-reaction (GPR) model of S. cerevisiae_iND750 and the method of constraint-based analysis, we first calculated the metabolic flux distribution of S. cere-visiae_iND750. Then we calculated the deletion impact of 438 calculable genes, one by one, on the metabolic flux redistribution of S. cere-visiae_iND750. Next we analyzed the correlation between v (describing deletion impact of one gene) and d (connection degree of one gene) and the correlation between v and Vgene (flux sum controlled by one gene), and found that both of them were not of linear relation. Furthermore, we sought out 38 important genes that most greatly affected the metabolic flux distribution, and determined their functional subsystems. We also found that many of these key genes were related to many but not several subsystems. Because the in silico model of S. cere-visiae_iND750 has been tested by many ex-periments, thus is credible, we can conclude that the result we obtained has biological sig-nificance.
基金supported by the Scientific Innovation Group for Youths of Sichuan Province under Grant No.2019JDTD0017。
文摘Liquefaction is one of the most destructive phenomena caused by earthquakes,which has been studied in the issues of potential,triggering and hazard analysis.The strain energy approach is a common method to investigate liquefaction potential.In this study,two Artificial Neural Network(ANN)models were developed to estimate the liquefaction resistance of sandy soil based on the capacity strain energy concept(W)by using laboratory test data.A large database was collected from the literature.One group of the dataset was utilized for validating the process in order to prevent overtraining the presented model.To investigate the complex influence of fine content(FC)on liquefaction resistance,according to previous studies,the second database was arranged by samples with FC of less than 28%and was used to train the second ANN model.Then,two presented ANN models in this study,in addition to four extra available models,were applied to an additional 20 new samples for comparing their results to show the capability and accuracy of the presented models herein.Furthermore,a parametric sensitivity analysis was performed through Monte Carlo Simulation(MCS)to evaluate the effects of parameters and their uncertainties on the liquefaction resistance of soils.According to the results,the developed models provide a higher accuracy prediction performance than the previously publishedmodels.The sensitivity analysis illustrated that the uncertainties of grading parameters significantly affect the liquefaction resistance of soils.
文摘With the combination of a new theoretical formula, physical simulation experiments, the technology of artificial neural network and database, an intelligent system for the prediction of sheet metal drawing capability is constructed for the first time. A modified criterion for sheet metal drawing capability is proposed in this paper, namely, the Technological Limiting Drawing Ratio, TLDR = f(R, n, s, t, F, μ,r_d,r_p…). Based on the studies of other scholars, a new formula is derived to predict the TLDR in this paper. Then a series of orthogonal physical simulation experiments are designed to investigate the effect of technological parameters on the TLDR, and the results are analyzed in the paper. Then the predicting system is constructed with the combination of the theoretical formula, orthogonal experiments, the technology of artifocial neural network and database. The predicted results show good agreements with experimental data, so it can be used to avoid the blindness in the selection of sheet metal before stamping. The system operates under the Windows operating system, and it supports the mechanism of Client/Server as well as Intranet, so the system has high engineering value.
基金support from the National Natural Science Foundation of China(Grant Nos.52074187,U1960105,and 52274388)Also,the authors thank for the open-ended fund from Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education,Wuhan University of Science and Technology(No.MADTOF2022B01).
文摘The rolling force model is the basis for reasonable selection of rolling equipment and optimization of rolling process,and the establishment of an accurate mathematical model as well as doing the corresponding parameter analysis has been the focus of research in this field for many years.Different modeling methods of the rolling force and their research progress were introduced,the main methods of which are the theoretical analysis,the finite element simulation,the artificial neural network modeling,the hybrid modeling of theory and neural network,as well as the hybrid modeling of finite element and neural network.Meanwhile,the application examples of rolling force models in thickness control,strip crown control,and schedule optimization were presented,and an outlook on the new directions of future development was made,including establishing new type of hybrid models,solving the black box problem,and realizing the multi-objective optimization accounting for some special requirements.
基金Project supported by the Key Program of the National Key Research and Development Program of China (Grant No. 2016YFA0501702)the National Natural Science Foundation of China (Grant No. 11674065)。
文摘Prion diseases are associated with the misfolding of the normal helical cellular form of prion protein (PrPC) into the β-sheet-rich scrapie form (PrPSc) and the subsequent aggregation of PrPSc into amyloid fibrils. Recent studies demonstrated that a naturally occurring variant V127 of human PrPC is intrinsically resistant to prion conversion and aggregation, and can completely prevent prion diseases. However, the underlying molecular mechanism remains elusive. Herein we perform multiple microsecond molecular dynamics simulations on both wildtype (WT) and V127 variant of human PrPC to understand at atomic level the protective effect of V127 variant. Our simulations show that G127V mutation not only increases the rigidity of the S2–H2 loop between strand-2 (S2) and helix-2 (H2), but also allosterically enhances the stability of the H2 C-terminal region. Interestingly, previous studies reported that animals with rigid S2–H2 loop usually do not develop prion diseases, and the increase in H2 C-terminal stability can prevent misfolding and oligomerization of prion protein. The allosteric paths from G/V127 to H2 C-terminal region are identified using dynamical network analyses. Moreover, community network analyses illustrate that G127V mutation enhances the global correlations and intra-molecular interactions of PrP, thus stabilizing the overall PrPC structure and inhibiting its conversion into PrPSc. This study provides mechanistic understanding of human V127 variant in preventing prion conversion which may be helpful for the rational design of potent anti-prion compounds.
