In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asy...In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.展开更多
Selective laser melting(SLM)plays a critical role in additive manufacturing,particularly in the fabrication of complex high-precision components.This study selects the AlSi10Mg alloy for its extensive use in the aeros...Selective laser melting(SLM)plays a critical role in additive manufacturing,particularly in the fabrication of complex high-precision components.This study selects the AlSi10Mg alloy for its extensive use in the aerospace and automotive industries,which require lightweight structures with superior thermal and mechanical properties.The thermal load induces residual tensile stress,leading to a decline in the geometric accuracy of the workpiece and causing cracks that reduce the fatigue life of the alloy.The rapid movement of the laser heat source during the material formation creates a localized and inhomogeneous temperature field in the powder bed.Significant temperature gradients are generated,resulting in thermal stresses and distortions within the part,affecting the quality of the molding.Therefore,understanding the effects of processing parameters and scanning strategies on the temperature field in SLM is crucial.To address these issues,this study proposes a multiscale method for predicting the complex transient temperature field during the manufacturing process based on the heat-conduction equation.Considering the influence of temperature on the material properties,a temperature-prediction model for discontinuous scanning paths in SLM and a temperature field-calculation model for irregular scanning paths are developed.The models are validated using finite-element results and are in excellent agreement.The analytical model is then used to investigate the effects of the laser power,scanning speed,and scanning spacing on the temperature distribution.The results reveal that the peak temperature decreases exponentially with increasing scanning speed and increases linearly with increasing laser power.In addition,with increasing scanning spacing,the peak temperature of the adjacent tracks near the observation point decreases linearly.These findings are critical for optimizing the SLM-process parameters and improving the material-forming quality.展开更多
Parallel robots with SCARA（selective compliance assembly robot arm） motions are utilized widely in the field of high speed pick-and-place manipulation. Error modeling for these robots generally simplifies the parall...Parallel robots with SCARA（selective compliance assembly robot arm） motions are utilized widely in the field of high speed pick-and-place manipulation. Error modeling for these robots generally simplifies the parallelogram structures included by the robots as a link. As the established error model fails to reflect the error feature of the parallelogram structures, the effect of accuracy design and kinematic calibration based on the error model come to be undermined. An error modeling methodology is proposed to establish an error model of parallel robots with parallelogram structures. The error model can embody the geometric errors of all joints, including the joints of parallelogram structures. Thus it can contain more exhaustively the factors that reduce the accuracy of the robot. Based on the error model and some sensitivity indices defined in the sense of statistics, sensitivity analysis is carried out. Accordingly, some atlases are depicted to express each geometric error’s influence on the moving platform’s pose errors. From these atlases, the geometric errors that have greater impact on the accuracy of the moving platform are identified, and some sensitive areas where the pose errors of the moving platform are extremely sensitive to the geometric errors are also figured out. By taking into account the error factors which are generally neglected in all existing modeling methods, the proposed modeling method can thoroughly disclose the process of error transmission and enhance the efficacy of accuracy design and calibration.展开更多
Earth’s internal core and crustal magnetic fields,as measured by geomagnetic satellites like MSS-1(Macao Science Satellite-1)and Swarm,are vital for understanding core dynamics and tectonic evolution.To model these i...Earth’s internal core and crustal magnetic fields,as measured by geomagnetic satellites like MSS-1(Macao Science Satellite-1)and Swarm,are vital for understanding core dynamics and tectonic evolution.To model these internal magnetic fields accurately,data selection based on specific criteria is often employed to minimize the influence of rapidly changing current systems in the ionosphere and magnetosphere.However,the quantitative impact of various data selection criteria on internal geomagnetic field modeling is not well understood.This study aims to address this issue and provide a reference for constructing and applying geomagnetic field models.First,we collect the latest MSS-1 and Swarm satellite magnetic data and summarize widely used data selection criteria in geomagnetic field modeling.Second,we briefly describe the method to co-estimate the core,crustal,and large-scale magnetospheric fields using satellite magnetic data.Finally,we conduct a series of field modeling experiments with different data selection criteria to quantitatively estimate their influence.Our numerical experiments confirm that without selecting data from dark regions and geomagnetically quiet times,the resulting internal field differences at the Earth’s surface can range from tens to hundreds of nanotesla(nT).Additionally,we find that the uncertainties introduced into field models by different data selection criteria are significantly larger than the measurement accuracy of modern geomagnetic satellites.These uncertainties should be considered when utilizing constructed magnetic field models for scientific research and applications.展开更多
Real-time and reliable measurements of the effluent quality are essential to improve operating efficiency and reduce energy consumption for the wastewater treatment process.Due to the low accuracy and unstable perform...Real-time and reliable measurements of the effluent quality are essential to improve operating efficiency and reduce energy consumption for the wastewater treatment process.Due to the low accuracy and unstable performance of the traditional effluent quality measurements,we propose a selective ensemble extreme learning machine modeling method to enhance the effluent quality predictions.Extreme learning machine algorithm is inserted into a selective ensemble frame as the component model since it runs much faster and provides better generalization performance than other popular learning algorithms.Ensemble extreme learning machine models overcome variations in different trials of simulations for single model.Selective ensemble based on genetic algorithm is used to further exclude some bad components from all the available ensembles in order to reduce the computation complexity and improve the generalization performance.The proposed method is verified with the data from an industrial wastewater treatment plant,located in Shenyang,China.Experimental results show that the proposed method has relatively stronger generalization and higher accuracy than partial least square,neural network partial least square,single extreme learning machine and ensemble extreme learning machine model.展开更多
An experimental study of thermal de-NOx using NH3 as reductant in 02/C02 atmosphere with the effect of S02 and different additives was performed in a drop tube furnace. Results show that the optimum temperature win- d...An experimental study of thermal de-NOx using NH3 as reductant in 02/C02 atmosphere with the effect of S02 and different additives was performed in a drop tube furnace. Results show that the optimum temperature win- dow is 841-1184 ℃, and the optimum reaction temperature is about 900 ℃ with a de-NOx efficiency of 95.4%. A certain amount of S02 has an inhibiting effect on NO reduction. The effect of additives, including Na2C03, C2H5OH and FeCI3, on NO reduction by NH3 is also explored. The addition of Na2CO3 and FeCI3 is useful to widen the tem- perature window and shift the reaction to lower temperature for the efficiency is increased from 30.5% to 74.0% and 67.4% respectively at 800 ℃. Qualitatively, the modeling results using a detailed kinetic modeling mecha- nism represent well most of the process features. The effect of Na2CO3, C2H5OH and FeCI3 addition can be reproduced well by the Na2C03, C2H5OH and Fe（CO）5 sub-mechanism respectively. The reaction mechanism analysis shows that the effects of these additives on NO reduction are achieved mainly by promoting the produc- tion of OH radicals at lower temperature.展开更多
In order to investigate the hydrofining process of LCO for producing aromatics and gasoline,the selective hydrogenation of polycyclic aromatic hydrocarbons(PAHs),a major component of light cycle oil(LCO),was studied u...In order to investigate the hydrofining process of LCO for producing aromatics and gasoline,the selective hydrogenation of polycyclic aromatic hydrocarbons(PAHs),a major component of light cycle oil(LCO),was studied using a NiMoW/Al_(2)O_(3)catalyst.Based on the study of the reversible hydrogenation reaction,PAHs in the selective hydrogenation process could be effectively simulated by the modeled CH and CH_(2) groups,and the hydrodesulfurization and hydrodenitrogenation kinetic models could be further established in this process.The results showed that the kinetic models developed could fit the experimental data effectively and predict the content of S,N,and aromatics in the selective hydrogenation products of LCO.展开更多
Light olefins such as ethylene and propylene are important industrial feedstocks, having essential applications in the production of plastics, ethylbenzene, and ethylene dichloride [1]. Compared with the conventional ...Light olefins such as ethylene and propylene are important industrial feedstocks, having essential applications in the production of plastics, ethylbenzene, and ethylene dichloride [1]. Compared with the conventional route, in which alkane steam cracking (SC) at high temperature is applied to produce ethylene and propylene, the catalytic ethane/propane non-oxidative dehydrogenation (EDH/PDH) possess the advantages of high selectivity and low energy consumption. Industrially, Pt is the major component to catalyze this reaction, but it suffers from low selectivity and fast deactivation because of favorable coke formation [2].展开更多
Photocatalytic reduction of CO2 to CO is a promising approach for storing solar energy in chemicals and mitigating the greenhouse effect of CO2.Our recent studies revealed that[(μ-bdt)Fe2(CO)6](1,bdt=benzene-1,2-dith...Photocatalytic reduction of CO2 to CO is a promising approach for storing solar energy in chemicals and mitigating the greenhouse effect of CO2.Our recent studies revealed that[(μ-bdt)Fe2(CO)6](1,bdt=benzene-1,2-dithiolato),a[FeFe]-hydrogenase model with a rigid and conjugate S-to-S bridge,was catalytically active for the selective photochemical reduction of CO2 to CO,while its analogous complex[(μ-edt)Fe2(CO)6](2,edt=ethane-1,2-dithiolato)was inactive.In this study,it was found that the turnover number of 1 for CO evolution reached 710 for the 1/[Ru(bpy)3]2+/BIH(BIH=1,3-dimethyl-2-phenyl-2,3-dihydro-1H-benzo[d]-imidazole)system under optimal conditions over 4.5 h of visible-light irradiation,with a turnover frequency of 7.12 min−1 in the first hour,a high selectivity of 97%for CO,and an internal quantum yield of 2.8%.Interestingly,the catalytic selectivity of 1 can be adjusted and even completely switched in a facile manner between the photochemical reductions of CO2 to CO and of protons to H2 simply by adding different amounts of triethanolamine to the catalytic system.The electron transfer in the photocatalytic system was studied by steady-state fluorescence and transient absorption spectroscopy,and a plausible mechanism for the photocatalytic reaction was proposed.展开更多
Recent reports on the selective laser melting(SLM)process under a vacuum or low ambient pressure have shown fewer defects and better surface quality of the as-printed products.Although the physical process of SLM in a...Recent reports on the selective laser melting(SLM)process under a vacuum or low ambient pressure have shown fewer defects and better surface quality of the as-printed products.Although the physical process of SLM in a vacuum has been investigated by high-speed imaging,the underlying mechanisms governing the heat transfer and molten flow are still not well understood.Herein,we first developed a mesoscopic model of SLM under variable ambient pressure based on our recent laser-welding studies.We simulated the transport phenomena of SLM 316L stainless steel powders under atmospheric and 100 Pa ambient pressure.For typical process parameters(laser power:200W;scanning speed:2m∙s^(-1);powder diameter:27 lm),the average surface temperature of the cavity approached 2800 K under atmospheric pressure,while it came close to 2300 K under 100 Pa pressure.More vigorous fluid flow(average speed:4m∙s^(-1))was observed under 100 Pa ambient pressure,because the pressure difference between the evaporation-induced surface pressure and the ambient pressure was relatively larger and drives the flow under lower pressure.It was also shown that there are periodical ripple flows(period:14ls)affecting the surface roughness of the as-printed track.Moreover,the molten flow was shown to be laminar because the Reynolds number is less than 400 and is far below the critical value of turbulence;thus,the viscous dissipation is significant.It was demonstrated that under a vacuum or lower ambient pressure,the ripple flow can be dissipated more easily by the viscous effect because the trajectory length of the ripple is longer;thus,the surface quality of the tracks is improved.To summarize,our model elucidates the physical mechanisms of the interesting transport phenomena that have been observed in independent experimental studies of the SLM process under variable ambient pressure,which could be a powerful tool for optimizing the SLM process in the future.展开更多
Bone wound healing is a highly dynamic and precisely controlled process through which damaged bone undergoes repair and complete regeneration. External factors can alter this process, leading to delayed or failed bone...Bone wound healing is a highly dynamic and precisely controlled process through which damaged bone undergoes repair and complete regeneration. External factors can alter this process, leading to delayed or failed bone wound healing. The findings of recent studies suggest that the use of selective serotonin reuptake inhibitors(SSRIs) can reduce bone mass, precipitate osteoporotic fractures and increase the rate of dental implant failure. With 10% of Americans prescribed antidepressants, the potential of SSRIs to impair bone healing may adversely affect millions of patients’ ability to heal after sustaining trauma. Here, we investigate the effect of the SSRI sertraline on bone healing through pre-treatment with(10 mg·kg-1sertraline in drinking water, n = 26) or without(control, n = 30) SSRI followed by the creation of a 5-mm calvarial defect. Animals were randomized into three surgical groups:(a) empty/sham,(b) implanted with a DermaMatrix scaffold soak-loaded with sterile PBS or(c) DermaMatrix soak-loaded with542.5 ng BMP2. SSRI exposure continued until sacrifice in the exposed groups at 4 weeks after surgery. Sertraline exposure resulted in decreased bone healing with significant decreases in trabecular thickness, trabecular number and osteoclast dysfunction while significantly increasing mature collagen fiber formation. These findings indicate that sertraline exposure can impair bone wound healing through disruption of bone repair and regeneration while promoting or defaulting to scar formation within the defect site.展开更多
To solve the increasing model complexity due to several input variables and large correlations under variable load conditions,a dynamic modeling method combining a kernel extreme learning machine(KELM)and principal co...To solve the increasing model complexity due to several input variables and large correlations under variable load conditions,a dynamic modeling method combining a kernel extreme learning machine(KELM)and principal component analysis(PCA)was proposed and applied to the prediction of nitrogen oxide(NO_(x))concentration at the outlet of a selective catalytic reduction(SCR)denitrification system.First,PCA is applied to the feature information extraction of input data,and the current and previous sequence values of the extracted information are used as the inputs of the KELM model to reflect the dynamic characteristics of the NO_(x)concentration at the SCR outlet.Then,the model takes the historical data of the NO_(x)concentration at the SCR outlet as the model input to improve its accuracy.Finally,an optimization algorithm is used to determine the optimal parameters of the model.Compared with the Gaussian process regression,long short-term memory,and convolutional neural network models,the prediction errors are reduced by approximately 78.4%,67.6%,and 59.3%,respectively.The results indicate that the proposed dynamic model structure is reliable and can accurately predict NO_(x)concentrations at the outlet of the SCR system.展开更多
Selecting the optimal model helps decision-makers to reduce the uncertainty in the slope calculation process.The uncertainty quantification process using root-mean-square error(RMSE)has limitations.It can obscure loca...Selecting the optimal model helps decision-makers to reduce the uncertainty in the slope calculation process.The uncertainty quantification process using root-mean-square error(RMSE)has limitations.It can obscure local uncertainty features and neglect the statistical characteristics of uncertainty,which may hinder decision-makers'understanding and model selection.展开更多
Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model ...Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones.展开更多
As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. Ther...As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. There exists a gap in research on the detection and response to attacks on Medium Access Control (MAC) mechanisms themselves, which would lead to service outages between nodes. Classifying exploitation and deceptive jamming attacks on control mechanisms is particularly challengingdue to their resemblance to normal heavy communication patterns. Accordingly, this paper proposes a machine learning-based selective attack mitigation model that detects DoS attacks on wireless networks by monitoring packet log data. Based on the type of detected attack, it implements effective corresponding mitigation techniques to restore performance to nodes whose availability has been compromised. Experimental results reveal that the accuracy of the proposed model is 14% higher than that of a baseline anomaly detection model. Further, the appropriate mitigation techniques selected by the proposed system based on the attack type improve the average throughput by more than 440% compared to the case without a response.展开更多
BACKGROUND Relieving pain is central to the early management of knee osteoarthritis,with a plethora of pharmacological agents licensed for this purpose.Intra-articular corticosteroid injections are a widely used optio...BACKGROUND Relieving pain is central to the early management of knee osteoarthritis,with a plethora of pharmacological agents licensed for this purpose.Intra-articular corticosteroid injections are a widely used option,albeit with variable efficacy.AIM To develop a machine learning(ML)model that predicts which patients will benefit from corticosteroid injections.METHODS Data from two prospective cohort studies[Osteoarthritis(OA)Initiative and Multicentre OA Study]was combined.The primary outcome was patientreported pain score following corticosteroid injection,assessed using the Western Ontario and McMaster Universities OA pain scale,with significant change defined using minimally clinically important difference and meaningful within person change.A ML algorithm was developed,utilizing linear discriminant analysis,to predict symptomatic improvement,and examine the association between pain scores and patient factors by calculating the sensitivity,specificity,positive predictive value,negative predictive value,accuracy,and F2 score.RESULTS A total of 330 patients were included,with a mean age of 63.4(SD:8.3).The mean Western Ontario and McMaster Universities OA pain score was 5.2(SD:4.1),with only 25.5%of patients achieving significant improvement in pain following corticosteroid injection.The ML model generated an accuracy of 67.8%(95%confidence interval:64.6%-70.9%),F1 score of 30.8%,and an area under the curve score of 0.60.CONCLUSION The model demonstrated feasibility to assist clinicians with decision-making in patient selection for corticosteroid injections.Further studies are required to improve the model prior to testing in clinical settings.展开更多
In clinical research,subgroup analysis can help identify patient groups that respond better or worse to specific treatments,improve therapeutic effect and safety,and is of great significance in precision medicine.This...In clinical research,subgroup analysis can help identify patient groups that respond better or worse to specific treatments,improve therapeutic effect and safety,and is of great significance in precision medicine.This article considers subgroup analysis methods for longitudinal data containing multiple covariates and biomarkers.We divide subgroups based on whether a linear combination of these biomarkers exceeds a predetermined threshold,and assess the heterogeneity of treatment effects across subgroups using the interaction between subgroups and exposure variables.Quantile regression is used to better characterize the global distribution of the response variable and sparsity penalties are imposed to achieve variable selection of covariates and biomarkers.The effectiveness of our proposed methodology for both variable selection and parameter estimation is verified through random simulations.Finally,we demonstrate the application of this method by analyzing data from the PA.3 trial,further illustrating the practicality of the method proposed in this paper.展开更多
Selecting proper descriptors(also known feature selection,FS)is key in the process of establishing mechanical properties prediction model of hot-rolled microalloyed steels by using machine learning(ML)algorithm.FS met...Selecting proper descriptors(also known feature selection,FS)is key in the process of establishing mechanical properties prediction model of hot-rolled microalloyed steels by using machine learning(ML)algorithm.FS methods based on data-driving can reduce the redundancy of data features and improve the prediction accuracy of mechanical properties.Based on the collected data of hot-rolled microalloyed steels,the association rules are used to mine the correlation information between the data.High-quality feature subsets are selected by the proposed FS method(FS method based on genetic algorithm embedding,GAMIC).Compared with the common FS method,it is shown on dataset that GAMIC selects feature subsets more appropriately.Six different ML algorithms are trained and tested for mechanical properties prediction.The result shows that the root-mean-square error of yield strength,tensile strength and elongation based on limit gradient enhancement(XGBoost)algorithm is 21.95 MPa,20.85 MPa and 1.96%,the correlation coefficient(R^(2))is 0.969,0.968 and 0.830,and the mean absolute error is 16.84 MPa,15.83 MPa and 1.48%,respectively,showing the best prediction performance.Finally,SHapley Additive exPlanation is used to further explore the influence of feature variables on mechanical properties.GAMIC feature selection method proposed is universal,which provides a basis for the development of high-precision mechanical property prediction model.展开更多
The AlMgScZr high-strength aluminum alloy fabricated by selective laser melting(SLM)technology exhibits a“bimodal microstructure”,resulting in significant non-uniform deformation during thermal deformation.This stud...The AlMgScZr high-strength aluminum alloy fabricated by selective laser melting(SLM)technology exhibits a“bimodal microstructure”,resulting in significant non-uniform deformation during thermal deformation.This study investigates the flow behavior of SLM-processed AlMgScZr aluminum alloy utilizing the Gleeble-1500D thermal simulation machine.The true stress-strain curves were amended based on the friction theory.Through determining the Zener-Hollomon parameters,the correlation between flow stress,deformation temperature,and strain rate during the high-temperature thermoplastic deformation of SLM-processed AlMgScZr aluminum alloy with a“bimodal microstructure”was established.In addition,the microstructural evolution during thermal deformation was analyzed.The results indicated that the predicted flow stress values obtained from the Arrhenius constitutive equation with coupled correction of thermal deformation parameters closely matched the experimental values.The correlation coefficient and the average absolute relative error of the corrected model were 0.999 and 2.766%,respectively,accurately predicting the thermoplastic deformation behavior of SLM-processed high-strength aluminum alloy with a“bimodal microstructure”.Furthermore,hot processing maps at different strains were established,identifying stable and unstable regions under different deformation conditions.Microstructural observations revealed different thermal deformation mechanisms under various deformation temperatures.Specifically,dynamic recrystallization characteristics dominated the microstructure at lower temperatures(300-360℃),while dynamic recovery was dominant at higher temperatures(390-500℃).展开更多
In this paper,a feature selection method for determining input parameters in antenna modeling is proposed.In antenna modeling,the input feature of artificial neural network(ANN)is geometric parameters.The selection cr...In this paper,a feature selection method for determining input parameters in antenna modeling is proposed.In antenna modeling,the input feature of artificial neural network(ANN)is geometric parameters.The selection criteria contain correlation and sensitivity between the geometric parameter and the electromagnetic(EM)response.Maximal information coefficient(MIC),an exploratory data mining tool,is introduced to evaluate both linear and nonlinear correlations.The EM response range is utilized to evaluate the sensitivity.The wide response range corresponding to varying values of a parameter implies the parameter is highly sensitive and the narrow response range suggests the parameter is insensitive.Only the parameter which is highly correlative and sensitive is selected as the input of ANN,and the sampling space of the model is highly reduced.The modeling of a wideband and circularly polarized antenna is studied as an example to verify the effectiveness of the proposed method.The number of input parameters decreases from8 to 4.The testing errors of|S_(11)|and axis ratio are reduced by8.74%and 8.95%,respectively,compared with the ANN with no feature selection.展开更多
基金Supported by the National Natural Science Foundation of China(12261018)Universities Key Laboratory of Mathematical Modeling and Data Mining in Guizhou Province(2023013)。
文摘In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.
基金supported by National Natural Science Foundation of the China Youth Program(Grant No.52205485)Sichuan Youth Fund Program of China(Grant No.2025ZNSFSC1275)the Young Scientific Research Team Cultivation Program of SUES(Grant No.QNTD202112)。
文摘Selective laser melting(SLM)plays a critical role in additive manufacturing,particularly in the fabrication of complex high-precision components.This study selects the AlSi10Mg alloy for its extensive use in the aerospace and automotive industries,which require lightweight structures with superior thermal and mechanical properties.The thermal load induces residual tensile stress,leading to a decline in the geometric accuracy of the workpiece and causing cracks that reduce the fatigue life of the alloy.The rapid movement of the laser heat source during the material formation creates a localized and inhomogeneous temperature field in the powder bed.Significant temperature gradients are generated,resulting in thermal stresses and distortions within the part,affecting the quality of the molding.Therefore,understanding the effects of processing parameters and scanning strategies on the temperature field in SLM is crucial.To address these issues,this study proposes a multiscale method for predicting the complex transient temperature field during the manufacturing process based on the heat-conduction equation.Considering the influence of temperature on the material properties,a temperature-prediction model for discontinuous scanning paths in SLM and a temperature field-calculation model for irregular scanning paths are developed.The models are validated using finite-element results and are in excellent agreement.The analytical model is then used to investigate the effects of the laser power,scanning speed,and scanning spacing on the temperature distribution.The results reveal that the peak temperature decreases exponentially with increasing scanning speed and increases linearly with increasing laser power.In addition,with increasing scanning spacing,the peak temperature of the adjacent tracks near the observation point decreases linearly.These findings are critical for optimizing the SLM-process parameters and improving the material-forming quality.
基金Supported by National Natural Science Foundation of China(Grant No.51305222)National Key Scientific and Technological Program of China(Grant No.2013ZX04001-021)
文摘Parallel robots with SCARA（selective compliance assembly robot arm） motions are utilized widely in the field of high speed pick-and-place manipulation. Error modeling for these robots generally simplifies the parallelogram structures included by the robots as a link. As the established error model fails to reflect the error feature of the parallelogram structures, the effect of accuracy design and kinematic calibration based on the error model come to be undermined. An error modeling methodology is proposed to establish an error model of parallel robots with parallelogram structures. The error model can embody the geometric errors of all joints, including the joints of parallelogram structures. Thus it can contain more exhaustively the factors that reduce the accuracy of the robot. Based on the error model and some sensitivity indices defined in the sense of statistics, sensitivity analysis is carried out. Accordingly, some atlases are depicted to express each geometric error’s influence on the moving platform’s pose errors. From these atlases, the geometric errors that have greater impact on the accuracy of the moving platform are identified, and some sensitive areas where the pose errors of the moving platform are extremely sensitive to the geometric errors are also figured out. By taking into account the error factors which are generally neglected in all existing modeling methods, the proposed modeling method can thoroughly disclose the process of error transmission and enhance the efficacy of accuracy design and calibration.
基金supported by the National Natural Science Foundation of China(42250101)the Macao Foundation。
文摘Earth’s internal core and crustal magnetic fields,as measured by geomagnetic satellites like MSS-1(Macao Science Satellite-1)and Swarm,are vital for understanding core dynamics and tectonic evolution.To model these internal magnetic fields accurately,data selection based on specific criteria is often employed to minimize the influence of rapidly changing current systems in the ionosphere and magnetosphere.However,the quantitative impact of various data selection criteria on internal geomagnetic field modeling is not well understood.This study aims to address this issue and provide a reference for constructing and applying geomagnetic field models.First,we collect the latest MSS-1 and Swarm satellite magnetic data and summarize widely used data selection criteria in geomagnetic field modeling.Second,we briefly describe the method to co-estimate the core,crustal,and large-scale magnetospheric fields using satellite magnetic data.Finally,we conduct a series of field modeling experiments with different data selection criteria to quantitatively estimate their influence.Our numerical experiments confirm that without selecting data from dark regions and geomagnetically quiet times,the resulting internal field differences at the Earth’s surface can range from tens to hundreds of nanotesla(nT).Additionally,we find that the uncertainties introduced into field models by different data selection criteria are significantly larger than the measurement accuracy of modern geomagnetic satellites.These uncertainties should be considered when utilizing constructed magnetic field models for scientific research and applications.
基金supported by National Natural Science Foundation of China (Nos. 61203102 and 60874057)Postdoctoral Science Foundation of China (No. 20100471464)
文摘Real-time and reliable measurements of the effluent quality are essential to improve operating efficiency and reduce energy consumption for the wastewater treatment process.Due to the low accuracy and unstable performance of the traditional effluent quality measurements,we propose a selective ensemble extreme learning machine modeling method to enhance the effluent quality predictions.Extreme learning machine algorithm is inserted into a selective ensemble frame as the component model since it runs much faster and provides better generalization performance than other popular learning algorithms.Ensemble extreme learning machine models overcome variations in different trials of simulations for single model.Selective ensemble based on genetic algorithm is used to further exclude some bad components from all the available ensembles in order to reduce the computation complexity and improve the generalization performance.The proposed method is verified with the data from an industrial wastewater treatment plant,located in Shenyang,China.Experimental results show that the proposed method has relatively stronger generalization and higher accuracy than partial least square,neural network partial least square,single extreme learning machine and ensemble extreme learning machine model.
基金Supported by the National Natural Science Foundation of China(51206096)
文摘An experimental study of thermal de-NOx using NH3 as reductant in 02/C02 atmosphere with the effect of S02 and different additives was performed in a drop tube furnace. Results show that the optimum temperature win- dow is 841-1184 ℃, and the optimum reaction temperature is about 900 ℃ with a de-NOx efficiency of 95.4%. A certain amount of S02 has an inhibiting effect on NO reduction. The effect of additives, including Na2C03, C2H5OH and FeCI3, on NO reduction by NH3 is also explored. The addition of Na2CO3 and FeCI3 is useful to widen the tem- perature window and shift the reaction to lower temperature for the efficiency is increased from 30.5% to 74.0% and 67.4% respectively at 800 ℃. Qualitatively, the modeling results using a detailed kinetic modeling mecha- nism represent well most of the process features. The effect of Na2CO3, C2H5OH and FeCI3 addition can be reproduced well by the Na2C03, C2H5OH and Fe（CO）5 sub-mechanism respectively. The reaction mechanism analysis shows that the effects of these additives on NO reduction are achieved mainly by promoting the produc- tion of OH radicals at lower temperature.
基金financially supported by the SINOPEC Research and Develepment Project (No.120051-1)
文摘In order to investigate the hydrofining process of LCO for producing aromatics and gasoline,the selective hydrogenation of polycyclic aromatic hydrocarbons(PAHs),a major component of light cycle oil(LCO),was studied using a NiMoW/Al_(2)O_(3)catalyst.Based on the study of the reversible hydrogenation reaction,PAHs in the selective hydrogenation process could be effectively simulated by the modeled CH and CH_(2) groups,and the hydrodesulfurization and hydrodenitrogenation kinetic models could be further established in this process.The results showed that the kinetic models developed could fit the experimental data effectively and predict the content of S,N,and aromatics in the selective hydrogenation products of LCO.
基金supported by the U.S.Department of EnergyOffice of Science,Office of Basic Energy Sciences,Chemical Sciences,Geosciences,and Biosciences Division,Catalysis Science Program to the SUNCAT Center for Interface Science and Catalysis。
文摘Light olefins such as ethylene and propylene are important industrial feedstocks, having essential applications in the production of plastics, ethylbenzene, and ethylene dichloride [1]. Compared with the conventional route, in which alkane steam cracking (SC) at high temperature is applied to produce ethylene and propylene, the catalytic ethane/propane non-oxidative dehydrogenation (EDH/PDH) possess the advantages of high selectivity and low energy consumption. Industrially, Pt is the major component to catalyze this reaction, but it suffers from low selectivity and fast deactivation because of favorable coke formation [2].
文摘Photocatalytic reduction of CO2 to CO is a promising approach for storing solar energy in chemicals and mitigating the greenhouse effect of CO2.Our recent studies revealed that[(μ-bdt)Fe2(CO)6](1,bdt=benzene-1,2-dithiolato),a[FeFe]-hydrogenase model with a rigid and conjugate S-to-S bridge,was catalytically active for the selective photochemical reduction of CO2 to CO,while its analogous complex[(μ-edt)Fe2(CO)6](2,edt=ethane-1,2-dithiolato)was inactive.In this study,it was found that the turnover number of 1 for CO evolution reached 710 for the 1/[Ru(bpy)3]2+/BIH(BIH=1,3-dimethyl-2-phenyl-2,3-dihydro-1H-benzo[d]-imidazole)system under optimal conditions over 4.5 h of visible-light irradiation,with a turnover frequency of 7.12 min−1 in the first hour,a high selectivity of 97%for CO,and an internal quantum yield of 2.8%.Interestingly,the catalytic selectivity of 1 can be adjusted and even completely switched in a facile manner between the photochemical reductions of CO2 to CO and of protons to H2 simply by adding different amounts of triethanolamine to the catalytic system.The electron transfer in the photocatalytic system was studied by steady-state fluorescence and transient absorption spectroscopy,and a plausible mechanism for the photocatalytic reaction was proposed.
基金This research was supported by the National Science Fund for Excellent Young Scholars(52022033)the National Key Research and Development Program of China(2017YFE0100100 and 2018YFB1105300)+1 种基金was partially supported by the Government of Perm Krai(S-26/794)the Russian Foundation for Basic Research(16-48-590208).
文摘Recent reports on the selective laser melting(SLM)process under a vacuum or low ambient pressure have shown fewer defects and better surface quality of the as-printed products.Although the physical process of SLM in a vacuum has been investigated by high-speed imaging,the underlying mechanisms governing the heat transfer and molten flow are still not well understood.Herein,we first developed a mesoscopic model of SLM under variable ambient pressure based on our recent laser-welding studies.We simulated the transport phenomena of SLM 316L stainless steel powders under atmospheric and 100 Pa ambient pressure.For typical process parameters(laser power:200W;scanning speed:2m∙s^(-1);powder diameter:27 lm),the average surface temperature of the cavity approached 2800 K under atmospheric pressure,while it came close to 2300 K under 100 Pa pressure.More vigorous fluid flow(average speed:4m∙s^(-1))was observed under 100 Pa ambient pressure,because the pressure difference between the evaporation-induced surface pressure and the ambient pressure was relatively larger and drives the flow under lower pressure.It was also shown that there are periodical ripple flows(period:14ls)affecting the surface roughness of the as-printed track.Moreover,the molten flow was shown to be laminar because the Reynolds number is less than 400 and is far below the critical value of turbulence;thus,the viscous dissipation is significant.It was demonstrated that under a vacuum or lower ambient pressure,the ripple flow can be dissipated more easily by the viscous effect because the trajectory length of the ripple is longer;thus,the surface quality of the tracks is improved.To summarize,our model elucidates the physical mechanisms of the interesting transport phenomena that have been observed in independent experimental studies of the SLM process under variable ambient pressure,which could be a powerful tool for optimizing the SLM process in the future.
基金supported by a grant from the Musculoskeletal Transplant Foundation (JC)the National Institute of Health, the National Institute of Aging [NIH-NIA PO1-AG036675] (ME, WDH)+4 种基金in part by the Department of Veterans Affairs (VA Merit Award BX000333, ACL 1I01CX000930-01, WDH)funded through a training grant from the National Institutes of Health National Institute of Dental and Craniofacial Research [5T32DE017551]S.H. is funded through a fellowship from the National Institutes of Health National Institute of Dental and Craniofacial Research [5F32DE02471202]supported by the National Institutes of Health National Institute of General Medicine [P30GM103331]
文摘Bone wound healing is a highly dynamic and precisely controlled process through which damaged bone undergoes repair and complete regeneration. External factors can alter this process, leading to delayed or failed bone wound healing. The findings of recent studies suggest that the use of selective serotonin reuptake inhibitors(SSRIs) can reduce bone mass, precipitate osteoporotic fractures and increase the rate of dental implant failure. With 10% of Americans prescribed antidepressants, the potential of SSRIs to impair bone healing may adversely affect millions of patients’ ability to heal after sustaining trauma. Here, we investigate the effect of the SSRI sertraline on bone healing through pre-treatment with(10 mg·kg-1sertraline in drinking water, n = 26) or without(control, n = 30) SSRI followed by the creation of a 5-mm calvarial defect. Animals were randomized into three surgical groups:(a) empty/sham,(b) implanted with a DermaMatrix scaffold soak-loaded with sterile PBS or(c) DermaMatrix soak-loaded with542.5 ng BMP2. SSRI exposure continued until sacrifice in the exposed groups at 4 weeks after surgery. Sertraline exposure resulted in decreased bone healing with significant decreases in trabecular thickness, trabecular number and osteoclast dysfunction while significantly increasing mature collagen fiber formation. These findings indicate that sertraline exposure can impair bone wound healing through disruption of bone repair and regeneration while promoting or defaulting to scar formation within the defect site.
基金The National Natural Science Foundation of China(No.71471060)the Natural Science Foundation of Hebei Province(No.E2018502111)。
文摘To solve the increasing model complexity due to several input variables and large correlations under variable load conditions,a dynamic modeling method combining a kernel extreme learning machine(KELM)and principal component analysis(PCA)was proposed and applied to the prediction of nitrogen oxide(NO_(x))concentration at the outlet of a selective catalytic reduction(SCR)denitrification system.First,PCA is applied to the feature information extraction of input data,and the current and previous sequence values of the extracted information are used as the inputs of the KELM model to reflect the dynamic characteristics of the NO_(x)concentration at the SCR outlet.Then,the model takes the historical data of the NO_(x)concentration at the SCR outlet as the model input to improve its accuracy.Finally,an optimization algorithm is used to determine the optimal parameters of the model.Compared with the Gaussian process regression,long short-term memory,and convolutional neural network models,the prediction errors are reduced by approximately 78.4%,67.6%,and 59.3%,respectively.The results indicate that the proposed dynamic model structure is reliable and can accurately predict NO_(x)concentrations at the outlet of the SCR system.
基金supported by the National Key Research and Development Program of China:Core Technologies and Software Systems of Geospatial Intelligence(Project No.:2021YFB3900900).
文摘Selecting the optimal model helps decision-makers to reduce the uncertainty in the slope calculation process.The uncertainty quantification process using root-mean-square error(RMSE)has limitations.It can obscure local uncertainty features and neglect the statistical characteristics of uncertainty,which may hinder decision-makers'understanding and model selection.
基金Supported partially by the Post Doctoral Natural Science Foundation of China(2013M532118,2015T81082)the National Natural Science Foundation of China(61573364,61273177,61503066)+2 种基金the State Key Laboratory of Synthetical Automation for Process Industriesthe National High Technology Research and Development Program of China(2015AA043802)the Scientific Research Fund of Liaoning Provincial Education Department(L2013272)
文摘Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones.
基金supported by the Ministry of Trade,Industry and Energy(MOTIE)under Training Industrial Security Specialist for High-Tech Industry(RS-2024-00415520)supervised by the Korea Institute for Advancement of Technology(KIAT)the Ministry of Science and ICT(MSIT)under the ICT Challenge and Advanced Network of HRD(ICAN)Program(No.IITP-2022-RS-2022-00156310)supervised by the Institute of Information&Communication Technology Planning&Evaluation(IITP).
文摘As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. There exists a gap in research on the detection and response to attacks on Medium Access Control (MAC) mechanisms themselves, which would lead to service outages between nodes. Classifying exploitation and deceptive jamming attacks on control mechanisms is particularly challengingdue to their resemblance to normal heavy communication patterns. Accordingly, this paper proposes a machine learning-based selective attack mitigation model that detects DoS attacks on wireless networks by monitoring packet log data. Based on the type of detected attack, it implements effective corresponding mitigation techniques to restore performance to nodes whose availability has been compromised. Experimental results reveal that the accuracy of the proposed model is 14% higher than that of a baseline anomaly detection model. Further, the appropriate mitigation techniques selected by the proposed system based on the attack type improve the average throughput by more than 440% compared to the case without a response.
基金Supported by National Institute For Health and Care Research,No.NIHR302632.
文摘BACKGROUND Relieving pain is central to the early management of knee osteoarthritis,with a plethora of pharmacological agents licensed for this purpose.Intra-articular corticosteroid injections are a widely used option,albeit with variable efficacy.AIM To develop a machine learning(ML)model that predicts which patients will benefit from corticosteroid injections.METHODS Data from two prospective cohort studies[Osteoarthritis(OA)Initiative and Multicentre OA Study]was combined.The primary outcome was patientreported pain score following corticosteroid injection,assessed using the Western Ontario and McMaster Universities OA pain scale,with significant change defined using minimally clinically important difference and meaningful within person change.A ML algorithm was developed,utilizing linear discriminant analysis,to predict symptomatic improvement,and examine the association between pain scores and patient factors by calculating the sensitivity,specificity,positive predictive value,negative predictive value,accuracy,and F2 score.RESULTS A total of 330 patients were included,with a mean age of 63.4(SD:8.3).The mean Western Ontario and McMaster Universities OA pain score was 5.2(SD:4.1),with only 25.5%of patients achieving significant improvement in pain following corticosteroid injection.The ML model generated an accuracy of 67.8%(95%confidence interval:64.6%-70.9%),F1 score of 30.8%,and an area under the curve score of 0.60.CONCLUSION The model demonstrated feasibility to assist clinicians with decision-making in patient selection for corticosteroid injections.Further studies are required to improve the model prior to testing in clinical settings.
基金Supported by the Natural Science Foundation of Fujian Province(2022J011177,2024J01903)the Key Project of Fujian Provincial Education Department(JZ230054)。
文摘In clinical research,subgroup analysis can help identify patient groups that respond better or worse to specific treatments,improve therapeutic effect and safety,and is of great significance in precision medicine.This article considers subgroup analysis methods for longitudinal data containing multiple covariates and biomarkers.We divide subgroups based on whether a linear combination of these biomarkers exceeds a predetermined threshold,and assess the heterogeneity of treatment effects across subgroups using the interaction between subgroups and exposure variables.Quantile regression is used to better characterize the global distribution of the response variable and sparsity penalties are imposed to achieve variable selection of covariates and biomarkers.The effectiveness of our proposed methodology for both variable selection and parameter estimation is verified through random simulations.Finally,we demonstrate the application of this method by analyzing data from the PA.3 trial,further illustrating the practicality of the method proposed in this paper.
基金supported by the National Key Research and Development Program of China(Grant No.2021YFB3702404)the National Natural Science Foundation of China(Grant No.52104370)+4 种基金the Reviving-Liaoning Excellence Plan(XLYC2203186)Science and Technology Special Projects of Liaoning Province(Grant No.2022JH25/10200001)the Postdoctoral Research Fund for Northeastern(Grant No.20210203)Independent Projects of Basic Scientific Research(ZZ2021005)CITIC Niobium Steel Development Award Fund(2022-M1824).
文摘Selecting proper descriptors(also known feature selection,FS)is key in the process of establishing mechanical properties prediction model of hot-rolled microalloyed steels by using machine learning(ML)algorithm.FS methods based on data-driving can reduce the redundancy of data features and improve the prediction accuracy of mechanical properties.Based on the collected data of hot-rolled microalloyed steels,the association rules are used to mine the correlation information between the data.High-quality feature subsets are selected by the proposed FS method(FS method based on genetic algorithm embedding,GAMIC).Compared with the common FS method,it is shown on dataset that GAMIC selects feature subsets more appropriately.Six different ML algorithms are trained and tested for mechanical properties prediction.The result shows that the root-mean-square error of yield strength,tensile strength and elongation based on limit gradient enhancement(XGBoost)algorithm is 21.95 MPa,20.85 MPa and 1.96%,the correlation coefficient(R^(2))is 0.969,0.968 and 0.830,and the mean absolute error is 16.84 MPa,15.83 MPa and 1.48%,respectively,showing the best prediction performance.Finally,SHapley Additive exPlanation is used to further explore the influence of feature variables on mechanical properties.GAMIC feature selection method proposed is universal,which provides a basis for the development of high-precision mechanical property prediction model.
基金Project(22KJB430023)supported by the Natural Science Foundation for Colleges and Universities of Jiangsu Province,ChinaProject(1172922101)supported by the Youth Science and Technology Innovation Project of Jiangsu University of Science and Technology,China。
文摘The AlMgScZr high-strength aluminum alloy fabricated by selective laser melting(SLM)technology exhibits a“bimodal microstructure”,resulting in significant non-uniform deformation during thermal deformation.This study investigates the flow behavior of SLM-processed AlMgScZr aluminum alloy utilizing the Gleeble-1500D thermal simulation machine.The true stress-strain curves were amended based on the friction theory.Through determining the Zener-Hollomon parameters,the correlation between flow stress,deformation temperature,and strain rate during the high-temperature thermoplastic deformation of SLM-processed AlMgScZr aluminum alloy with a“bimodal microstructure”was established.In addition,the microstructural evolution during thermal deformation was analyzed.The results indicated that the predicted flow stress values obtained from the Arrhenius constitutive equation with coupled correction of thermal deformation parameters closely matched the experimental values.The correlation coefficient and the average absolute relative error of the corrected model were 0.999 and 2.766%,respectively,accurately predicting the thermoplastic deformation behavior of SLM-processed high-strength aluminum alloy with a“bimodal microstructure”.Furthermore,hot processing maps at different strains were established,identifying stable and unstable regions under different deformation conditions.Microstructural observations revealed different thermal deformation mechanisms under various deformation temperatures.Specifically,dynamic recrystallization characteristics dominated the microstructure at lower temperatures(300-360℃),while dynamic recovery was dominant at higher temperatures(390-500℃).
基金National Natural Science Foundation of China(62161048)Sichuan Science and Technology Program(2022NSFSC0547,2022ZYD0109)。
文摘In this paper,a feature selection method for determining input parameters in antenna modeling is proposed.In antenna modeling,the input feature of artificial neural network(ANN)is geometric parameters.The selection criteria contain correlation and sensitivity between the geometric parameter and the electromagnetic(EM)response.Maximal information coefficient(MIC),an exploratory data mining tool,is introduced to evaluate both linear and nonlinear correlations.The EM response range is utilized to evaluate the sensitivity.The wide response range corresponding to varying values of a parameter implies the parameter is highly sensitive and the narrow response range suggests the parameter is insensitive.Only the parameter which is highly correlative and sensitive is selected as the input of ANN,and the sampling space of the model is highly reduced.The modeling of a wideband and circularly polarized antenna is studied as an example to verify the effectiveness of the proposed method.The number of input parameters decreases from8 to 4.The testing errors of|S_(11)|and axis ratio are reduced by8.74%and 8.95%,respectively,compared with the ANN with no feature selection.
