Objective: To investigate the effects of plant-derived phenolic compounds(i.e. caffeic acid, cinnamic acid, ferulic acid and vanillic acid) on the production of quorum sensing regulated virulence factors such as pyocy...Objective: To investigate the effects of plant-derived phenolic compounds(i.e. caffeic acid, cinnamic acid, ferulic acid and vanillic acid) on the production of quorum sensing regulated virulence factors such as pyocyanin, biofilm formation and swarming motility of Pseudomonas aeruginosa(P. aeruginosa) isolates.Methods: Fourteen clinical P. aeruginosa isolates obtained from urine samples and P. aeruginosa PA01 strain were included in the study. The antibacterial effects of phenolic compounds were screened by well diffusion assay. Pyocyanin and biofilm activity were measured from culture supernatants and the absorbance values were measured using a spectrophotometer. Swarming plates supplemented with phenolic acids were point inoculated with P. aeruginosa strains and the ability to swarm was determined by measuring the distance of swarming from the central inoculation site.Results: Tested phenolic compounds reduced the production of pyocyanin and biofilm formation without affecting growth compared to untreated cultures. Moreover, these compounds blocked about 50% of biofilm production and swarming motility in P. aeruginosa isolates.Conclusions: We may suggest that if swarming and consecutive biofilm formation could be inhibited by the natural products as shown in our study, the bacteria could not attach to the surfaces and produce chronic infections. Antimicrobials and natural products could be combined and the dosage of antimicrobials could be reduced to overcome antimicrobial resistance and drug side effects.展开更多
From the organization of animal flocks to the emergence of swarming behaviors in bacterial suspension,populations of motile organisms at all scales display coherent collective motion.Recent studies showed that the ani...From the organization of animal flocks to the emergence of swarming behaviors in bacterial suspension,populations of motile organisms at all scales display coherent collective motion.Recent studies showed that the anisotropic interaction between active particles plays a key role in the phase behaviors.Here we investigate the collective behaviors of based-active Janus particles that experience an anisotropic interaction of which the orientation is opposite to the direction of active force by using Langevin dynamics simulations in two dimensional space.Interestingly,the system shows emergence of collective swarming states upon increasing the total area fraction of particles,which is not observed in systems without anisotropic interaction or activity.The threshold for emergence of swarming states decreases as particle activity or interaction strength increases.We have also performed basic kinetic analysis to reproduce the essential features of the simulation results.Our results demonstrate that anisotropic interactions at the individual level are sufficient to set homogeneous active particles into stable directed motion.展开更多
Cellular network operators have problems to test their network without affecting their user experience. Testingnetwork performance in a loaded situation is a challenge for the network operator because network performa...Cellular network operators have problems to test their network without affecting their user experience. Testingnetwork performance in a loaded situation is a challenge for the network operator because network performance differswhen it has more load on the radio access part. Therefore, in this paper, deploying swarming drones is proposed to loadthe cellular network and scan/test the network performance more realistically. Besides, manual swarming dronenavigation is not efficient enough to detect problematic regions. Hence, particle swarm optimization is proposed to bedeployed on swarming drone to find the regions where there are performance issues. Swarming drone communicationshelps to deploy the particle swarm optimization (PSO) method on them. Loading and testing swarm separation help tohave almost non-stochastic received signal level as an objective function. Moreover, there are some situations that morethan one network parameter should be used to find a problematic region in the cellular network. It is also proposed toapply multi-objective PSO to find more multi-parameter network optimization at the same time.展开更多
This paper is devoted to introducing an optimization algorithm which is devised on a basis of ordinary differential equation model describing the process of animal swarming. By several numerical simulations, the natur...This paper is devoted to introducing an optimization algorithm which is devised on a basis of ordinary differential equation model describing the process of animal swarming. By several numerical simulations, the nature of the optimization algorithm is clarified. Especially, if parameters included in the algorithm are suitably set, our scheme can show very good performance even in higher dimensional problems.展开更多
We consider an anisotropic swarm model with an attraction/repulsion function and study its aggregation properties. It is shown that the swarm members will aggregate and eventually form a cohesive cluster of finite siz...We consider an anisotropic swarm model with an attraction/repulsion function and study its aggregation properties. It is shown that the swarm members will aggregate and eventually form a cohesive cluster of finite size around the swarm center in a finite time. Moreover, we extend our results to more general attraction/repulsion functions. Numerical simulations demonstrate that all agents will eventually enter into and remain in a bounded region around the swarm center which may exhibit complex spiral motion due to asymmetry of the coupling structure. The model in this paper is more general than isotropic swarms and our results provide further insight into the effect of the interaction pattern on individual motion in a swarm system.展开更多
A common assumption of coverage path planning research is a static environment.Such environments require only a single visit to each area to achieve coverage.However,some real-world environments are characterised by t...A common assumption of coverage path planning research is a static environment.Such environments require only a single visit to each area to achieve coverage.However,some real-world environments are characterised by the presence of unexpected,dynamic obstacles.They require areas to be revisited periodically to maintain an accurate coverage map,as well as reactive obstacle avoidance.This paper proposes a novel swarmbased control algorithm for multi-robot exploration and repeated coverage in environments with unknown,dynamic obstacles.The algorithm combines two elements:frontier-led swarming for driving exploration by a group of robots,and pheromone-based stigmergy for controlling repeated coverage while avoiding obstacles.We tested the performance of our approach on heterogeneous and homogeneous groups of mobile robots in different environments.We measure both repeated coverage performance and obstacle avoidance ability.Through a series of comparison experiments,we demonstrate that our proposed strategy has superior performance to recently presented multi-robot repeated coverage methodologies.展开更多
In recent years,the integration of stochastic techniques,especially those based on artificial neural networks,has emerged as a pivotal advancement in the field of computational fluid dynamics.These techniques offer a ...In recent years,the integration of stochastic techniques,especially those based on artificial neural networks,has emerged as a pivotal advancement in the field of computational fluid dynamics.These techniques offer a powerful framework for the analysis of complex fluid flow phenomena and address the uncertainties inherent in fluid dynamics systems.Following this trend,the current investigation portrays the design and construction of an important technique named swarming optimized neuroheuristic intelligence with the competency of artificial neural networks to analyze nonlinear viscoelastic magneto-hydrodynamic Prandtl-Eyring fluid flow model,with diffusive magnetic layers effect along an extended sheet.The currently designed computational technique is established using inverse multiquadric radial basis activation function through the hybridization of a well-known global searching technique of particle swarm optimization and sequential quadratic programming,a technique capable of rapid convergence locally.The most appropriate scaling group involved transformations that are implemented on governing equations of the suggested fluidic model to convert it from a system of nonlinear partial differential equations into a dimensionless form of a third-order nonlinear ordinary differential equation.The transformed/reduced fluid flow model is solved for sundry variations of physical quantities using the designed scheme and outcomes are matched consistently with Adam's numerical technique with negligible magnitude of absolute errors and mean square errors.Moreover,it is revealed that the velocity of the fluid depreciates in the presence of a strong magnetic field effect.The efficacy of the designed solver is depicted evidently through rigorous statistical observations via exhaustive numerical experimentation of the fluidic problem.展开更多
The current study relates to designing a swarming computational paradigm to solve the influenza disease system(IDS).The nonlinear system’s mathematical form depends upon four classes:susceptible individuals,infected ...The current study relates to designing a swarming computational paradigm to solve the influenza disease system(IDS).The nonlinear system’s mathematical form depends upon four classes:susceptible individuals,infected people,recovered individuals and cross-immune people.The solutions of the IDS are provided by using the artificial neural networks(ANNs)together with the swarming computational paradigm-based particle swarmoptimization(PSO)and interior-point scheme(IPA)that are the global and local search approaches.The ANNs-PSO-IPA has never been applied to solve the IDS.Instead a merit function in the sense of mean square error is constructed using the differential form of each class of the IDS and then optimized by the PSOIPA.The correctness and accuracy of the scheme are observed to perform the comparative analysis of the obtained IDS results with the Adams solutions(reference solutions).An absolute error in suitable measures shows the precision of the proposed ANNs procedures and the optimization efficiency of the PSOIPA.Furthermore,the reliability and competence of the proposed computing method are enhanced through the statistical performances.展开更多
The purpose of this research is to construct an integrated neuro swarming scheme using the procedures of the artificial neural networks(ANNs)with the use of global search particle swarm optimization(PSO)along with the...The purpose of this research is to construct an integrated neuro swarming scheme using the procedures of the artificial neural networks(ANNs)with the use of global search particle swarm optimization(PSO)along with the competent local search interior-point programming(IPP)called as ANN-PSOIPP.The proposed computational scheme is implemented for the numerical simulations of the third order nonlinear delay differential Emden-Fowler model(TON-DD-EFM).The TON-DD-EFM is based on two types along with the particulars of shape factor,delayed terms,and singular points.A merit function is performed using the optimization of PSOIPP to find the solutions to the TON-DD-EFM.The effectiveness of the ANN-PSOIPP is certified through the comparison with the exact results for solving four examples of the TON-DD-EFM.The scheme’s efficiency is observed by performing the absolute error in suitable measures found around 10−04 to 10−07.Furthermore,the statistical-based assessments for 100 trials are provided to compute the accuracy,stability,and constancy of the ANNPSOIPP for solving the TON-DD-EFM.展开更多
The present study is related to design a stochastic framework for the numerical treatment of the Van der Pol heartbeat model(VP-HBM)using the feedforward artificial neural networks(ANNs)under the optimization of parti...The present study is related to design a stochastic framework for the numerical treatment of the Van der Pol heartbeat model(VP-HBM)using the feedforward artificial neural networks(ANNs)under the optimization of particle swarm optimization(PSO)hybridized with the active-set algorithm(ASA),i.e.,ANNs-PSO-ASA.The global search PSO scheme and local refinement of ASA are used as an optimization procedure in this study.An error-based merit function is defined using the differential VP-HBM form as well as the initial conditions.The optimization of the merit function is accomplished using the hybrid computing performances of PSO-ASA.The designed performance of ANNs-PSO-ASA is implemented for the numerical treatment of the VP-HBM dynamics by fluctuating the pulse shape adjustment terms,external forcing factor and damping coefficient with fixed ventricular contraction period.To perform the correctness of the present scheme,the obtained numerical results through the designed ANN-PSO-ASA will be compared with the Adams numerical method.The statistical investigations with larger dataset are provided using the“mean absolute deviation”,“Theil’s inequality coefficient”and“variance account for”operators to perform the applicability,reliability,and effectiveness of the designed ANNs-PSO-ASA scheme for solving the VP-HBM.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
This article investigates the combination of magnetic data from the MSS-1 and Swarm satellites for improved investigations of Earth’s magnetic field and Geospace.The study highlights the complementary nature of polar...This article investigates the combination of magnetic data from the MSS-1 and Swarm satellites for improved investigations of Earth’s magnetic field and Geospace.The study highlights the complementary nature of polar-orbiting(Swarm)and low-inclination(MSS-1)satellites in geomagnetic modelling and monitoring large-scale magnetospheric contributions.Data from close encounters between MSS-1 and Swarm(intersatellite distance<100 km)confirm the excellent data quality of the two satellite missions(<1 nT median difference in scalar intensity F)and allow for data calibration and validation and investigations of in-situ ionospheric currents.The reason for a small but consistent difference(F as measured by MSS-1 is 0.5 to 1.0 nT larger than that measured by Swarm)is unknown.Combining MSS-1’s low-inclination data with Swarm’s near-polar observations significantly enhances the spatial-temporal resolution of Earth’s magnetic field models,allowing for new opportunities for studying both rapid core field variations at low latitudes and the local-time dependence of large-scale magnetospheric current systems.A joint analysis of magnetic data from six satellites during the May 2024 geomagnetic storm reveals a clear dawn-dusk asymmetry,with equatorial magnetic disturbances during dusk being approximately 150 nT more negative than during dawn.展开更多
Measurements from geomagnetic satellites continue to underpin advances in geomagnetic field models that describe Earth's internally generated magnetic field.Here,we present a new field model,MSCM,that integrates v...Measurements from geomagnetic satellites continue to underpin advances in geomagnetic field models that describe Earth's internally generated magnetic field.Here,we present a new field model,MSCM,that integrates vector and scalar data from the Swarm,China Seismo-Electromagnetic Satellite(CSES),and Macao Science Satellite-1(MSS-1)missions.The model spans from 2014.0 to 2024.5,incorporating the core,lithospheric,and magnetospheric fields,and it shows characteristics similar to other published models based on different data.For the first time,we demonstrate that it is possible to successfully construct a geomagnetic field model that incorporates CSES vector data,albeit one in which the radial and azimuthal CSES vector components are Huber downweighted.We further show that data from the MSS-1 can be integrated within an explicitly smoothed,fully time-dependent model description.Using the MSCM,we identify new behavior of the South Atlantic Anomaly,the broad region of low magnetic field intensity over the southern Atlantic.This prominent feature appears split into a western part and an eastern part,each with its own intensity minimum.Since 2015,the principal western minimum has undergone only modest intensity decreases of 290 nT and westward motion of 20 km per year,whereas the recently formed eastern minimum has shown a 2–3 times greater intensity drop of 730 nT with no apparent east-west motion.展开更多
Accurate modeling of Earth's ionospheric F-region currents is essential for refining geomagnetic field models and understanding magnetosphere-ionosphere coupling.In this study,we develop averaged models to charact...Accurate modeling of Earth's ionospheric F-region currents is essential for refining geomagnetic field models and understanding magnetosphere-ionosphere coupling.In this study,we develop averaged models to characterize F-region currents using magnetic data from the MSS-1(Macao Science Satellite-1) and Swarm satellite missions.Our approach employs a toroidal field representation,utilizing spherical harmonics to capture spatial variations and Fourier series to represent temporal dynamics.Two models,Model-A and Model-B,derived from distinct datasets,are constructed to represent current patterns at altitudes of 450 km and 512 km,respectively.Our models successfully capture the primary spatial structures and seasonal variations of polar field-aligned currents.Additionally,they accurately reproduce the localized inter-hemispheric field-aligned currents observed in mid and low latitudes during solstices,particularly between 14:00 and 16:00 magnetic local times.These findings enhance our understanding of ionospheric F-region currents and contribute to more precise geomagnetic field modeling.展开更多
The endpoint carbon content in the converter is critical for the quality of steel products,and accurately predicting this parameter is an effective way to reduce alloy consumption and improve smelting efficiency.Howev...The endpoint carbon content in the converter is critical for the quality of steel products,and accurately predicting this parameter is an effective way to reduce alloy consumption and improve smelting efficiency.However,most scholars currently focus on modifying methods to enhance model accuracy,while overlooking the extent to which input parameters influence accuracy.To address this issue,in this study,a prediction model for the endpoint carbon content in the converter was developed using factor analysis(FA)and support vector machine(SVM)optimized by improved particle swarm optimization(IPSO).Analysis of the factors influencing the endpoint carbon content during the converter smelting process led to the identification of 21 input parameters.Subsequently,FA was used to reduce the dimensionality of the data and applied to the prediction model.The results demonstrate that the performance of the FA-IPSO-SVM model surpasses several existing methods,such as twin support vector regression and support vector machine.The model achieves hit rates of 89.59%,96.21%,and 98.74%within error ranges of±0.01%,±0.015%,and±0.02%,respectively.Finally,based on the prediction results obtained by sequentially removing input parameters,the parameters were classified into high influence(5%-7%),medium influence(2%-5%),and low influence(0-2%)categories according to their varying degrees of impact on prediction accuracy.This classi-fication provides a reference for selecting input parameters in future prediction models for endpoint carbon content.展开更多
Teacher–student relationships play a vital role in improving college students’academic performance and the quality of higher education.However,empirical studies with substantial data-driven insights remain limited.T...Teacher–student relationships play a vital role in improving college students’academic performance and the quality of higher education.However,empirical studies with substantial data-driven insights remain limited.To address this gap,this study collected 3278 questionnaires from seven universities across four provinces in China to analyze the key factors affecting college students’academic performance.A machine learning framework,CQFOA-KELM,was developed by enhancing the Fruit Fly Optimization Algorithm(FOA)with Covariance Matrix Adaptation Evolution Strategy(CMAES)and Quadratic Approximation(QA).CQFOA significantly improved population diversity and was validated on the IEEE CEC2017 benchmark functions.The CQFOA-KELM model achieved an accuracy of 98.15%and a sensitivity of 98.53%in predicting college students’academic performance.Additionally,it effectively identified the key factors influencing academic performance through the feature selection process.展开更多
The proposed hybrid optimization algorithm integrates particle swarm optimizatio(PSO)with Ant Colony Optimization(ACO)to improve a number of pitfalls within PSO methods traditionally considered and/or applied to indus...The proposed hybrid optimization algorithm integrates particle swarm optimizatio(PSO)with Ant Colony Optimization(ACO)to improve a number of pitfalls within PSO methods traditionally considered and/or applied to industrial robots.Particle Swarm Optimization may frequently suffer from local optima and inaccuracies in identifying the geometric parameters,which are necessary for applications requiring high-accuracy performances.The proposed approach integrates pheromone-based learning of ACO with the D-H method of developing an error model;hence,the global search effectiveness together with the convergence accuracy is further improved.Comparison studies of the hybrid PSO-ACO algorithm show higher precision and effectiveness in the optimization of geometric error parameters compared to the traditional methods.This is a remarkable reduction of localization errors,thus yielding accuracy and reliability in industrial robotic systems,as the results show.This approach improves performance in those applications that demand high geometric calibration by reducing the geometric error.The paper provides an overview of input for developing robotics and automation,giving importance to precision in industrial engineering.The proposed hybrid methodology is a good way to enhance the working accuracy and effectiveness of industrial robots and shall enable their wide application to complex tasks that require a high degree of accuracy.展开更多
基金Supported by a grant from the Marmara University Scientific Research Fund(Project No.:SAG-B-110412-0079)
文摘Objective: To investigate the effects of plant-derived phenolic compounds(i.e. caffeic acid, cinnamic acid, ferulic acid and vanillic acid) on the production of quorum sensing regulated virulence factors such as pyocyanin, biofilm formation and swarming motility of Pseudomonas aeruginosa(P. aeruginosa) isolates.Methods: Fourteen clinical P. aeruginosa isolates obtained from urine samples and P. aeruginosa PA01 strain were included in the study. The antibacterial effects of phenolic compounds were screened by well diffusion assay. Pyocyanin and biofilm activity were measured from culture supernatants and the absorbance values were measured using a spectrophotometer. Swarming plates supplemented with phenolic acids were point inoculated with P. aeruginosa strains and the ability to swarm was determined by measuring the distance of swarming from the central inoculation site.Results: Tested phenolic compounds reduced the production of pyocyanin and biofilm formation without affecting growth compared to untreated cultures. Moreover, these compounds blocked about 50% of biofilm production and swarming motility in P. aeruginosa isolates.Conclusions: We may suggest that if swarming and consecutive biofilm formation could be inhibited by the natural products as shown in our study, the bacteria could not attach to the surfaces and produce chronic infections. Antimicrobials and natural products could be combined and the dosage of antimicrobials could be reduced to overcome antimicrobial resistance and drug side effects.
基金supported by the Ministry of Science and Technology(2016YFA0400904 and 2018YFA0208702)the National Natural Foundation of China(No.21973085,No.21833007,No.21790350,No.21673212,No.21521001,and No.21473165)+1 种基金the Fundamental Research Funds for the Central Universities(No.WK2340000074)Anhui Initiative in Quantum Information Technologies(No.AHY090200).
文摘From the organization of animal flocks to the emergence of swarming behaviors in bacterial suspension,populations of motile organisms at all scales display coherent collective motion.Recent studies showed that the anisotropic interaction between active particles plays a key role in the phase behaviors.Here we investigate the collective behaviors of based-active Janus particles that experience an anisotropic interaction of which the orientation is opposite to the direction of active force by using Langevin dynamics simulations in two dimensional space.Interestingly,the system shows emergence of collective swarming states upon increasing the total area fraction of particles,which is not observed in systems without anisotropic interaction or activity.The threshold for emergence of swarming states decreases as particle activity or interaction strength increases.We have also performed basic kinetic analysis to reproduce the essential features of the simulation results.Our results demonstrate that anisotropic interactions at the individual level are sufficient to set homogeneous active particles into stable directed motion.
文摘Cellular network operators have problems to test their network without affecting their user experience. Testingnetwork performance in a loaded situation is a challenge for the network operator because network performance differswhen it has more load on the radio access part. Therefore, in this paper, deploying swarming drones is proposed to loadthe cellular network and scan/test the network performance more realistically. Besides, manual swarming dronenavigation is not efficient enough to detect problematic regions. Hence, particle swarm optimization is proposed to bedeployed on swarming drone to find the regions where there are performance issues. Swarming drone communicationshelps to deploy the particle swarm optimization (PSO) method on them. Loading and testing swarm separation help tohave almost non-stochastic received signal level as an objective function. Moreover, there are some situations that morethan one network parameter should be used to find a problematic region in the cellular network. It is also proposed toapply multi-objective PSO to find more multi-parameter network optimization at the same time.
文摘This paper is devoted to introducing an optimization algorithm which is devised on a basis of ordinary differential equation model describing the process of animal swarming. By several numerical simulations, the nature of the optimization algorithm is clarified. Especially, if parameters included in the algorithm are suitably set, our scheme can show very good performance even in higher dimensional problems.
基金This work was supported by the National Natural Science Foundation of China (No. 10372002,60274001) and the National Key Basic Research and Develop-ment Program (No.2002CB312200).
文摘We consider an anisotropic swarm model with an attraction/repulsion function and study its aggregation properties. It is shown that the swarm members will aggregate and eventually form a cohesive cluster of finite size around the swarm center in a finite time. Moreover, we extend our results to more general attraction/repulsion functions. Numerical simulations demonstrate that all agents will eventually enter into and remain in a bounded region around the swarm center which may exhibit complex spiral motion due to asymmetry of the coupling structure. The model in this paper is more general than isotropic swarms and our results provide further insight into the effect of the interaction pattern on individual motion in a swarm system.
基金supported by the DEFENCE SCIENCE&TECHNOLOGY GROUP(DSTG)(9729)The Commonwealth of Australia supported this research through a Defence Science Partnerships agreement with the Australian Defence Science and Technology Group。
文摘A common assumption of coverage path planning research is a static environment.Such environments require only a single visit to each area to achieve coverage.However,some real-world environments are characterised by the presence of unexpected,dynamic obstacles.They require areas to be revisited periodically to maintain an accurate coverage map,as well as reactive obstacle avoidance.This paper proposes a novel swarmbased control algorithm for multi-robot exploration and repeated coverage in environments with unknown,dynamic obstacles.The algorithm combines two elements:frontier-led swarming for driving exploration by a group of robots,and pheromone-based stigmergy for controlling repeated coverage while avoiding obstacles.We tested the performance of our approach on heterogeneous and homogeneous groups of mobile robots in different environments.We measure both repeated coverage performance and obstacle avoidance ability.Through a series of comparison experiments,we demonstrate that our proposed strategy has superior performance to recently presented multi-robot repeated coverage methodologies.
文摘In recent years,the integration of stochastic techniques,especially those based on artificial neural networks,has emerged as a pivotal advancement in the field of computational fluid dynamics.These techniques offer a powerful framework for the analysis of complex fluid flow phenomena and address the uncertainties inherent in fluid dynamics systems.Following this trend,the current investigation portrays the design and construction of an important technique named swarming optimized neuroheuristic intelligence with the competency of artificial neural networks to analyze nonlinear viscoelastic magneto-hydrodynamic Prandtl-Eyring fluid flow model,with diffusive magnetic layers effect along an extended sheet.The currently designed computational technique is established using inverse multiquadric radial basis activation function through the hybridization of a well-known global searching technique of particle swarm optimization and sequential quadratic programming,a technique capable of rapid convergence locally.The most appropriate scaling group involved transformations that are implemented on governing equations of the suggested fluidic model to convert it from a system of nonlinear partial differential equations into a dimensionless form of a third-order nonlinear ordinary differential equation.The transformed/reduced fluid flow model is solved for sundry variations of physical quantities using the designed scheme and outcomes are matched consistently with Adam's numerical technique with negligible magnitude of absolute errors and mean square errors.Moreover,it is revealed that the velocity of the fluid depreciates in the presence of a strong magnetic field effect.The efficacy of the designed solver is depicted evidently through rigorous statistical observations via exhaustive numerical experimentation of the fluidic problem.
基金This research received funding support from the NSRF via the Program Man-agement Unit for Human Resources&Institutional Development,Research and Innovation(Grant Number B05F640092).
文摘The current study relates to designing a swarming computational paradigm to solve the influenza disease system(IDS).The nonlinear system’s mathematical form depends upon four classes:susceptible individuals,infected people,recovered individuals and cross-immune people.The solutions of the IDS are provided by using the artificial neural networks(ANNs)together with the swarming computational paradigm-based particle swarmoptimization(PSO)and interior-point scheme(IPA)that are the global and local search approaches.The ANNs-PSO-IPA has never been applied to solve the IDS.Instead a merit function in the sense of mean square error is constructed using the differential form of each class of the IDS and then optimized by the PSOIPA.The correctness and accuracy of the scheme are observed to perform the comparative analysis of the obtained IDS results with the Adams solutions(reference solutions).An absolute error in suitable measures shows the precision of the proposed ANNs procedures and the optimization efficiency of the PSOIPA.Furthermore,the reliability and competence of the proposed computing method are enhanced through the statistical performances.
基金This project is funded by National Research Council of Thailand(NRCT)and Khon Kaen University:N42A650291.
文摘The purpose of this research is to construct an integrated neuro swarming scheme using the procedures of the artificial neural networks(ANNs)with the use of global search particle swarm optimization(PSO)along with the competent local search interior-point programming(IPP)called as ANN-PSOIPP.The proposed computational scheme is implemented for the numerical simulations of the third order nonlinear delay differential Emden-Fowler model(TON-DD-EFM).The TON-DD-EFM is based on two types along with the particulars of shape factor,delayed terms,and singular points.A merit function is performed using the optimization of PSOIPP to find the solutions to the TON-DD-EFM.The effectiveness of the ANN-PSOIPP is certified through the comparison with the exact results for solving four examples of the TON-DD-EFM.The scheme’s efficiency is observed by performing the absolute error in suitable measures found around 10−04 to 10−07.Furthermore,the statistical-based assessments for 100 trials are provided to compute the accuracy,stability,and constancy of the ANNPSOIPP for solving the TON-DD-EFM.
基金This research received funding support from the NSRF via the Program Management Unit for Human Resources&Institutional Development,Research and Innovation(Grant Number B05F640088).
文摘The present study is related to design a stochastic framework for the numerical treatment of the Van der Pol heartbeat model(VP-HBM)using the feedforward artificial neural networks(ANNs)under the optimization of particle swarm optimization(PSO)hybridized with the active-set algorithm(ASA),i.e.,ANNs-PSO-ASA.The global search PSO scheme and local refinement of ASA are used as an optimization procedure in this study.An error-based merit function is defined using the differential VP-HBM form as well as the initial conditions.The optimization of the merit function is accomplished using the hybrid computing performances of PSO-ASA.The designed performance of ANNs-PSO-ASA is implemented for the numerical treatment of the VP-HBM dynamics by fluctuating the pulse shape adjustment terms,external forcing factor and damping coefficient with fixed ventricular contraction period.To perform the correctness of the present scheme,the obtained numerical results through the designed ANN-PSO-ASA will be compared with the Adams numerical method.The statistical investigations with larger dataset are provided using the“mean absolute deviation”,“Theil’s inequality coefficient”and“variance account for”operators to perform the applicability,reliability,and effectiveness of the designed ANNs-PSO-ASA scheme for solving the VP-HBM.
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the China National Space Administration (CNSA) and the Macao Foundation for operating the MSS-1satelliteThis work has been carried out as part of ESA’s Swarm DISC activities funded by ESA contract no.4000109587.
文摘This article investigates the combination of magnetic data from the MSS-1 and Swarm satellites for improved investigations of Earth’s magnetic field and Geospace.The study highlights the complementary nature of polar-orbiting(Swarm)and low-inclination(MSS-1)satellites in geomagnetic modelling and monitoring large-scale magnetospheric contributions.Data from close encounters between MSS-1 and Swarm(intersatellite distance<100 km)confirm the excellent data quality of the two satellite missions(<1 nT median difference in scalar intensity F)and allow for data calibration and validation and investigations of in-situ ionospheric currents.The reason for a small but consistent difference(F as measured by MSS-1 is 0.5 to 1.0 nT larger than that measured by Swarm)is unknown.Combining MSS-1’s low-inclination data with Swarm’s near-polar observations significantly enhances the spatial-temporal resolution of Earth’s magnetic field models,allowing for new opportunities for studying both rapid core field variations at low latitudes and the local-time dependence of large-scale magnetospheric current systems.A joint analysis of magnetic data from six satellites during the May 2024 geomagnetic storm reveals a clear dawn-dusk asymmetry,with equatorial magnetic disturbances during dusk being approximately 150 nT more negative than during dawn.
基金supported by the National Natural Science Foundation of China(Grant No.42274003)PWL was supported by Swarm DISC(Swarm Data,Innovation,and Science Cluster)+2 种基金funded by the European Space Agency(ESAContract No.4000109587)HFR acknowledges funding from the UK Natural Environment Research Council(Grant No.NE/V010867/1)。
文摘Measurements from geomagnetic satellites continue to underpin advances in geomagnetic field models that describe Earth's internally generated magnetic field.Here,we present a new field model,MSCM,that integrates vector and scalar data from the Swarm,China Seismo-Electromagnetic Satellite(CSES),and Macao Science Satellite-1(MSS-1)missions.The model spans from 2014.0 to 2024.5,incorporating the core,lithospheric,and magnetospheric fields,and it shows characteristics similar to other published models based on different data.For the first time,we demonstrate that it is possible to successfully construct a geomagnetic field model that incorporates CSES vector data,albeit one in which the radial and azimuthal CSES vector components are Huber downweighted.We further show that data from the MSS-1 can be integrated within an explicitly smoothed,fully time-dependent model description.Using the MSCM,we identify new behavior of the South Atlantic Anomaly,the broad region of low magnetic field intensity over the southern Atlantic.This prominent feature appears split into a western part and an eastern part,each with its own intensity minimum.Since 2015,the principal western minimum has undergone only modest intensity decreases of 290 nT and westward motion of 20 km per year,whereas the recently formed eastern minimum has shown a 2–3 times greater intensity drop of 730 nT with no apparent east-west motion.
基金supported by the National Natural Science Foundation of China (42250101)the Macao Foundation. The computation made use of the high-performance computing resources at the center of the MSS data processing and analysis。
文摘Accurate modeling of Earth's ionospheric F-region currents is essential for refining geomagnetic field models and understanding magnetosphere-ionosphere coupling.In this study,we develop averaged models to characterize F-region currents using magnetic data from the MSS-1(Macao Science Satellite-1) and Swarm satellite missions.Our approach employs a toroidal field representation,utilizing spherical harmonics to capture spatial variations and Fourier series to represent temporal dynamics.Two models,Model-A and Model-B,derived from distinct datasets,are constructed to represent current patterns at altitudes of 450 km and 512 km,respectively.Our models successfully capture the primary spatial structures and seasonal variations of polar field-aligned currents.Additionally,they accurately reproduce the localized inter-hemispheric field-aligned currents observed in mid and low latitudes during solstices,particularly between 14:00 and 16:00 magnetic local times.These findings enhance our understanding of ionospheric F-region currents and contribute to more precise geomagnetic field modeling.
基金financially supported by the National Natural Science Foundation of China(No.52174297).
文摘The endpoint carbon content in the converter is critical for the quality of steel products,and accurately predicting this parameter is an effective way to reduce alloy consumption and improve smelting efficiency.However,most scholars currently focus on modifying methods to enhance model accuracy,while overlooking the extent to which input parameters influence accuracy.To address this issue,in this study,a prediction model for the endpoint carbon content in the converter was developed using factor analysis(FA)and support vector machine(SVM)optimized by improved particle swarm optimization(IPSO).Analysis of the factors influencing the endpoint carbon content during the converter smelting process led to the identification of 21 input parameters.Subsequently,FA was used to reduce the dimensionality of the data and applied to the prediction model.The results demonstrate that the performance of the FA-IPSO-SVM model surpasses several existing methods,such as twin support vector regression and support vector machine.The model achieves hit rates of 89.59%,96.21%,and 98.74%within error ranges of±0.01%,±0.015%,and±0.02%,respectively.Finally,based on the prediction results obtained by sequentially removing input parameters,the parameters were classified into high influence(5%-7%),medium influence(2%-5%),and low influence(0-2%)categories according to their varying degrees of impact on prediction accuracy.This classi-fication provides a reference for selecting input parameters in future prediction models for endpoint carbon content.
文摘Teacher–student relationships play a vital role in improving college students’academic performance and the quality of higher education.However,empirical studies with substantial data-driven insights remain limited.To address this gap,this study collected 3278 questionnaires from seven universities across four provinces in China to analyze the key factors affecting college students’academic performance.A machine learning framework,CQFOA-KELM,was developed by enhancing the Fruit Fly Optimization Algorithm(FOA)with Covariance Matrix Adaptation Evolution Strategy(CMAES)and Quadratic Approximation(QA).CQFOA significantly improved population diversity and was validated on the IEEE CEC2017 benchmark functions.The CQFOA-KELM model achieved an accuracy of 98.15%and a sensitivity of 98.53%in predicting college students’academic performance.Additionally,it effectively identified the key factors influencing academic performance through the feature selection process.
文摘The proposed hybrid optimization algorithm integrates particle swarm optimizatio(PSO)with Ant Colony Optimization(ACO)to improve a number of pitfalls within PSO methods traditionally considered and/or applied to industrial robots.Particle Swarm Optimization may frequently suffer from local optima and inaccuracies in identifying the geometric parameters,which are necessary for applications requiring high-accuracy performances.The proposed approach integrates pheromone-based learning of ACO with the D-H method of developing an error model;hence,the global search effectiveness together with the convergence accuracy is further improved.Comparison studies of the hybrid PSO-ACO algorithm show higher precision and effectiveness in the optimization of geometric error parameters compared to the traditional methods.This is a remarkable reduction of localization errors,thus yielding accuracy and reliability in industrial robotic systems,as the results show.This approach improves performance in those applications that demand high geometric calibration by reducing the geometric error.The paper provides an overview of input for developing robotics and automation,giving importance to precision in industrial engineering.The proposed hybrid methodology is a good way to enhance the working accuracy and effectiveness of industrial robots and shall enable their wide application to complex tasks that require a high degree of accuracy.
