The surface correction to the quadrupole source term of the Ffowcs Williams and Hawkings integral in the frequency domain suffers from the computation of high-order derivatives of Green’s function.The far-field appro...The surface correction to the quadrupole source term of the Ffowcs Williams and Hawkings integral in the frequency domain suffers from the computation of high-order derivatives of Green’s function.The far-field approximations to the derivatives of Green’s function have been used without derivation and verification in previous work.In this work,we provide the detailed derivations of the far-field approximations to the derivatives of Green’s function.The binomial expansions for the derivatives of Green’s function and the far-field condition are employed during the derivations to circumvent the difficulties in computing the high-order derivatives.The approximations to the derivatives of Green’s function are systemically verified by using the benchmarks two-dimensional convecting vortex and the co-rotating vortex pair.In addition,we provide the derivations of the approximations to the multiple integrals of Green’s function by using the far-field approximations to the derivatives.展开更多
A rare sesquiterpene of hawkteasesquioid A(1),featuring a novel 5/6/5 tricyclic system containing a 6,5-spiroketal motif,was isolated from the bark of Litsea coreana Lévl.var.lanuginosa(hawk tea).The structure of...A rare sesquiterpene of hawkteasesquioid A(1),featuring a novel 5/6/5 tricyclic system containing a 6,5-spiroketal motif,was isolated from the bark of Litsea coreana Lévl.var.lanuginosa(hawk tea).The structure of this compound was elucidated based on the high-resolution electrospray ionization mass spectrometry(HRESIMS),one-dimensional(1D)and two-dimensional(2D)nuclear magnetic resonance(NMR)spectroscopy,and single-crystal X-ray diffraction data.The cytotoxicity of this compound was assessed on the A549,HT-29,and SW1990 cell lines.展开更多
In response to the increasing global energy demand and environmental pollution,microgrids have emerged as an innovative solution by integrating distributed energy resources(DERs),energy storage systems,and loads to im...In response to the increasing global energy demand and environmental pollution,microgrids have emerged as an innovative solution by integrating distributed energy resources(DERs),energy storage systems,and loads to improve energy efficiency and reliability.This study proposes a novel hybrid optimization algorithm,DE-HHO,combining differential evolution(DE)and Harris Hawks optimization(HHO)to address microgrid scheduling issues.The proposed method adopts a multi-objective optimization framework that simultaneously minimizes operational costs and environmental impacts.The DE-HHO algorithm demonstrates significant advantages in convergence speed and global search capability through the analysis of wind,solar,micro-gas turbine,and battery models.Comprehensive simulation tests show that DE-HHO converges rapidly within 10 iterations and achieves a 4.5%reduction in total cost compared to PSO and a 5.4%reduction compared to HHO.Specifically,DE-HHO attains an optimal total cost of$20,221.37,outperforming PSO($21,184.45)and HHO($21,372.24).The maximum cost obtained by DE-HHO is$23,420.55,with a mean of$21,615.77,indicating stability and cost control capabilities.These results highlight the effectiveness of DE-HHO in reducing operational costs and enhancing system stability for efficient and sustainable microgrid operation.展开更多
Virtualization is an indispensable part of the cloud for the objective of deploying different virtual servers over the same physical layer.However,the increase in the number of applications executing on the repositori...Virtualization is an indispensable part of the cloud for the objective of deploying different virtual servers over the same physical layer.However,the increase in the number of applications executing on the repositories results in increased overload due to the adoption of cloud services.Moreover,the migration of applications on the cloud with optimized resource allocation is a herculean task even though it is employed for minimizing the dilemma of allocating resources.In this paper,a Fire Hawk Optimization enabled Deep Learning Scheme(FHOEDLS)is proposed for minimizing the overload and optimizing the resource allocation on the hybrid cloud container architecture for migrating interoperability based applications This FHOEDLS achieves the load prediction through the utilization of deep CNN-GRU-AM model for attaining resource allocation and better migration of applications.It specifically adopted the Fire Hawk Optimization Algorithm(FHOA)for optimizing the parameters that influence the factors that aid in better interoperable application migration with improved resource allocation and minimized overhead.It considered the factors of resource capacity,transmission cost,demand,and predicted load into account during the formulation of the objective function utilized for resource allocation and application migration.The cloud simulation of this FHOEDLS is achieved using a container,Virtual Machine(VM),and Physical Machine(PM).The results of this proposed FHOEDLS confirmed a better resource capability of 0.418 and a minimized load of 0.0061.展开更多
In this work,we apply tunneling formalism to analyze charged particles tunneling across a hairy black hole horizon.Such black hole solutions are essential for frameworks based on Horndeski's gravity theory.Applyin...In this work,we apply tunneling formalism to analyze charged particles tunneling across a hairy black hole horizon.Such black hole solutions are essential for frameworks based on Horndeski's gravity theory.Applying a semi-classical technique,we examine the tunneling of charged particles from a hairy black hole and derive the generic tunneling spectrum of released particles,ignoring self-gravitational and interaction.It is studied to ignore the back-reaction impact of the radiated particle on the hairy black hole.We analyze the properties of the black hole,such as temperature and entropy,under the influence of quantum gravity and also observe that the firstorder correction is present.We study tunneling radiation produced by a charged field equation in the presence of a generalized uncertainty effect.We modify the semi-classical technique by using the generalized uncertainty principle,the WKB approximation,and surface gravity.展开更多
Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy output.This enhances the efficiency and reliability of renewable energy sys...Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy output.This enhances the efficiency and reliability of renewable energy systems.Forecasting approaches inform energy management strategies,reduce reliance on fossil fuels,and support the broader transition to sustainable energy solutions.The primary goal of this study is to introduce an effective methodology for estimating wind power through temporal data analysis.This research advances an optimized Multilayer Perceptron(MLP)model using recently proposedmetaheuristic optimization algorithms,namely the FireHawk Optimizer(FHO)and the Non-Monopolize Search(NO).A modified version of FHO,termed FHONO,is developed by integrating NO as a local search mechanism to enhance the exploration capability and address the shortcomings of the original FHO.The developed FHONO is then employed to optimize the MLP for enhanced wind power prediction.The effectiveness of the proposed FHONO-MLP model is validated using renowned datasets from wind turbines in France.The results of the comparative analysis between FHONO-MLP,conventionalMLP,and other optimized versions of MLP show that FHONO-MLP outperforms the others,achieving an average RootMean Square Error(RMSE)of 0.105,Mean Absolute Error(MAE)of 0.082,and Coefficient of Determination(R^(2))of 0.967 across all datasets.These findings underscore the significant enhancement in predictive accuracy provided by FHONO and demonstrate its effectiveness in improving wind power forecasting.展开更多
Early detection of Alzheimer’s disease(AD)is crucial,particularly in resource-constrained medical settings.This study introduces an optimized deep learning framework that conceptualizes neural networks as computatio...Early detection of Alzheimer’s disease(AD)is crucial,particularly in resource-constrained medical settings.This study introduces an optimized deep learning framework that conceptualizes neural networks as computational“sensors”for neurodegenerative diagnosis,incorporating feature selection,selective layer unfreezing,pruning,and algorithmic optimization.An enhanced lightweight hybrid DenseNet201 model is proposed,integrating layer pruning strategies for feature selection and bioinspired optimization techniques,including Genetic Algorithm(GA)and Harris Hawks Optimization(HHO),for hyperparameter tuning.Layer pruning helps identify and eliminate less significant features,while model parameter optimization further enhances performance by fine-tuning critical hyperparameters,improving convergence speed,and maximizing classification accuracy.GA is also used to reduce the number of selected features further.A detailed comparison of six AD classification model setups is provided to illustrate the variations and their impact on performance.Applying the lightweight hybrid DenseNet201 model for MRI-based AD classification yielded an impressive baseline F1 score of 98%.Overall feature reduction reached 51.75%,enhancing interpretability and lowering processing costs.The optimized models further demonstrated perfect generalization,achieving 100%classification accuracy.These findings underscore the potential of advanced optimization techniques in developing efficient and accurate AD diagnostic tools suitable for environments with limited computational resources.展开更多
Addressing the complex issue of emergency resource distribution center site selection in uncertain environments, this study was conducted to comprehensively consider factors such as uncertainty parameters and the urge...Addressing the complex issue of emergency resource distribution center site selection in uncertain environments, this study was conducted to comprehensively consider factors such as uncertainty parameters and the urgency of demand at disaster-affected sites. Firstly, urgency cost, economic cost, and transportation distance cost were identified as key objectives. The study applied fuzzy theory integration to construct a triangular fuzzy multi-objective site selection decision model. Next, the defuzzification theory transformed the fuzzy decision model into a precise one. Subsequently, an improved Chaotic Quantum Multi-Objective Harris Hawks Optimization (CQ-MOHHO) algorithm was proposed to solve the model. The CQ-MOHHO algorithm was shown to rapidly produce high-quality Pareto front solutions and identify optimal site selection schemes for emergency resource distribution centers through case studies. This outcome verified the feasibility and efficacy of the site selection decision model and the CQ-MOHHO algorithm. To further assess CQ-MOHHO’s performance, Zitzler-Deb-Thiele (ZDT) test functions, commonly used in multi-objective optimization, were employed. Comparisons with Multi-Objective Harris Hawks Optimization (MOHHO), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Multi-Objective Grey Wolf Optimizer (MOGWO) using Generational Distance (GD), Hypervolume (HV), and Inverted Generational Distance (IGD) metrics showed that CQ-MOHHO achieved superior global search ability, faster convergence, and higher solution quality. The CQ-MOHHO algorithm efficiently achieved a balance between multiple objectives, providing decision-makers with satisfactory solutions and a valuable reference for researching and applying emergency site selection problems.展开更多
Hybrid renewable energy systems(HRES)offer cost-effectiveness,low-emission power solutions,and reduced dependence on fossil fuels.However,the renewable energy allocation problem remains challenging due to complex syst...Hybrid renewable energy systems(HRES)offer cost-effectiveness,low-emission power solutions,and reduced dependence on fossil fuels.However,the renewable energy allocation problem remains challenging due to complex system interactions and multiple operational constraints.This study develops a novel Multi-Neighborhood Enhanced Harris Hawks Optimization(MNEHHO)algorithm to address the allocation of HRES components.The proposed approach integrates key technical parameters,including charge-discharge efficiency,storage device configurations,and renewable energy fraction.We formulate a comprehensive mathematical model that simultaneously minimizes levelized energy costs and pollutant emissions while maintaining system reliability.The MNEHHO algorithm employs multiple neighborhood structures to enhance solution diversity and exploration capabilities.The model’s effectiveness is validated through case studies across four distinct institutional energy demand profiles.Results demonstrate that our approach successfully generates practically feasible HRES configurations while achieving significant reductions in costs and emissions compared to conventional methods.The enhanced search mechanisms of MNEHHO show superior performance in avoiding local optima and achieving consistent solutions.Experimental results demonstrate concrete improvements in solution quality(up to 46% improvement in objective value)and computational efficiency(average coefficient of variance of 24%-27%)across diverse institutional settings.This confirms the robustness and scalability of our method under various operational scenarios,providing a reliable framework for solving renewable energy allocation problems.展开更多
To improve the quality of the illumination distribution,one novel indoor visible light communication(VLC)system,which is jointly assisted by the angle-diversity transceivers and simultaneous transmission and reflectio...To improve the quality of the illumination distribution,one novel indoor visible light communication(VLC)system,which is jointly assisted by the angle-diversity transceivers and simultaneous transmission and reflection-intelligent reflecting surface(STAR-IRS),has been proposed in this work.A Harris Hawks optimizer algorithm(HHOA)-based two-stage alternating iteration algorithm(TSAIA)is presented to jointly optimize the magnitude and uniformity of the received optical power.Besides,to demonstrate the superiority of the proposed strategy,several benchmark schemes are simulated and compared.Results showed that compared to other optimization strategies,the TSAIA scheme is more capable of balancing the average value and variance of the received optical power,when the maximal ratio combining(MRC)strategy is adopted at the receiver.Moreover,as the number of the STAR-IRS elements increases,the optical power variance of the system optimized by TSAIA scheme would become smaller while the average optical power would get larger.This study will benefit the design of received optical power distribution for indoor VLC systems.展开更多
基金National Numerical Windtunnel project,and the National Natural Science Foundation of China(Nos.11922214,91752118).
文摘The surface correction to the quadrupole source term of the Ffowcs Williams and Hawkings integral in the frequency domain suffers from the computation of high-order derivatives of Green’s function.The far-field approximations to the derivatives of Green’s function have been used without derivation and verification in previous work.In this work,we provide the detailed derivations of the far-field approximations to the derivatives of Green’s function.The binomial expansions for the derivatives of Green’s function and the far-field condition are employed during the derivations to circumvent the difficulties in computing the high-order derivatives.The approximations to the derivatives of Green’s function are systemically verified by using the benchmarks two-dimensional convecting vortex and the co-rotating vortex pair.In addition,we provide the derivations of the approximations to the multiple integrals of Green’s function by using the far-field approximations to the derivatives.
文摘A rare sesquiterpene of hawkteasesquioid A(1),featuring a novel 5/6/5 tricyclic system containing a 6,5-spiroketal motif,was isolated from the bark of Litsea coreana Lévl.var.lanuginosa(hawk tea).The structure of this compound was elucidated based on the high-resolution electrospray ionization mass spectrometry(HRESIMS),one-dimensional(1D)and two-dimensional(2D)nuclear magnetic resonance(NMR)spectroscopy,and single-crystal X-ray diffraction data.The cytotoxicity of this compound was assessed on the A549,HT-29,and SW1990 cell lines.
文摘In response to the increasing global energy demand and environmental pollution,microgrids have emerged as an innovative solution by integrating distributed energy resources(DERs),energy storage systems,and loads to improve energy efficiency and reliability.This study proposes a novel hybrid optimization algorithm,DE-HHO,combining differential evolution(DE)and Harris Hawks optimization(HHO)to address microgrid scheduling issues.The proposed method adopts a multi-objective optimization framework that simultaneously minimizes operational costs and environmental impacts.The DE-HHO algorithm demonstrates significant advantages in convergence speed and global search capability through the analysis of wind,solar,micro-gas turbine,and battery models.Comprehensive simulation tests show that DE-HHO converges rapidly within 10 iterations and achieves a 4.5%reduction in total cost compared to PSO and a 5.4%reduction compared to HHO.Specifically,DE-HHO attains an optimal total cost of$20,221.37,outperforming PSO($21,184.45)and HHO($21,372.24).The maximum cost obtained by DE-HHO is$23,420.55,with a mean of$21,615.77,indicating stability and cost control capabilities.These results highlight the effectiveness of DE-HHO in reducing operational costs and enhancing system stability for efficient and sustainable microgrid operation.
文摘Virtualization is an indispensable part of the cloud for the objective of deploying different virtual servers over the same physical layer.However,the increase in the number of applications executing on the repositories results in increased overload due to the adoption of cloud services.Moreover,the migration of applications on the cloud with optimized resource allocation is a herculean task even though it is employed for minimizing the dilemma of allocating resources.In this paper,a Fire Hawk Optimization enabled Deep Learning Scheme(FHOEDLS)is proposed for minimizing the overload and optimizing the resource allocation on the hybrid cloud container architecture for migrating interoperability based applications This FHOEDLS achieves the load prediction through the utilization of deep CNN-GRU-AM model for attaining resource allocation and better migration of applications.It specifically adopted the Fire Hawk Optimization Algorithm(FHOA)for optimizing the parameters that influence the factors that aid in better interoperable application migration with improved resource allocation and minimized overhead.It considered the factors of resource capacity,transmission cost,demand,and predicted load into account during the formulation of the objective function utilized for resource allocation and application migration.The cloud simulation of this FHOEDLS is achieved using a container,Virtual Machine(VM),and Physical Machine(PM).The results of this proposed FHOEDLS confirmed a better resource capability of 0.418 and a minimized load of 0.0061.
基金funded by the National Natural Science Foundation of China under Grant No.11975145。
文摘In this work,we apply tunneling formalism to analyze charged particles tunneling across a hairy black hole horizon.Such black hole solutions are essential for frameworks based on Horndeski's gravity theory.Applying a semi-classical technique,we examine the tunneling of charged particles from a hairy black hole and derive the generic tunneling spectrum of released particles,ignoring self-gravitational and interaction.It is studied to ignore the back-reaction impact of the radiated particle on the hairy black hole.We analyze the properties of the black hole,such as temperature and entropy,under the influence of quantum gravity and also observe that the firstorder correction is present.We study tunneling radiation produced by a charged field equation in the presence of a generalized uncertainty effect.We modify the semi-classical technique by using the generalized uncertainty principle,the WKB approximation,and surface gravity.
基金the Deanship of Graduate Studies and Scientific Research at University of Bisha,Saudi Arabia for funding this research work through the Promising Program under Grant Number(UB-Promising-42-1445).
文摘Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy output.This enhances the efficiency and reliability of renewable energy systems.Forecasting approaches inform energy management strategies,reduce reliance on fossil fuels,and support the broader transition to sustainable energy solutions.The primary goal of this study is to introduce an effective methodology for estimating wind power through temporal data analysis.This research advances an optimized Multilayer Perceptron(MLP)model using recently proposedmetaheuristic optimization algorithms,namely the FireHawk Optimizer(FHO)and the Non-Monopolize Search(NO).A modified version of FHO,termed FHONO,is developed by integrating NO as a local search mechanism to enhance the exploration capability and address the shortcomings of the original FHO.The developed FHONO is then employed to optimize the MLP for enhanced wind power prediction.The effectiveness of the proposed FHONO-MLP model is validated using renowned datasets from wind turbines in France.The results of the comparative analysis between FHONO-MLP,conventionalMLP,and other optimized versions of MLP show that FHONO-MLP outperforms the others,achieving an average RootMean Square Error(RMSE)of 0.105,Mean Absolute Error(MAE)of 0.082,and Coefficient of Determination(R^(2))of 0.967 across all datasets.These findings underscore the significant enhancement in predictive accuracy provided by FHONO and demonstrate its effectiveness in improving wind power forecasting.
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia(Grant No.KFU251428).
文摘Early detection of Alzheimer’s disease(AD)is crucial,particularly in resource-constrained medical settings.This study introduces an optimized deep learning framework that conceptualizes neural networks as computational“sensors”for neurodegenerative diagnosis,incorporating feature selection,selective layer unfreezing,pruning,and algorithmic optimization.An enhanced lightweight hybrid DenseNet201 model is proposed,integrating layer pruning strategies for feature selection and bioinspired optimization techniques,including Genetic Algorithm(GA)and Harris Hawks Optimization(HHO),for hyperparameter tuning.Layer pruning helps identify and eliminate less significant features,while model parameter optimization further enhances performance by fine-tuning critical hyperparameters,improving convergence speed,and maximizing classification accuracy.GA is also used to reduce the number of selected features further.A detailed comparison of six AD classification model setups is provided to illustrate the variations and their impact on performance.Applying the lightweight hybrid DenseNet201 model for MRI-based AD classification yielded an impressive baseline F1 score of 98%.Overall feature reduction reached 51.75%,enhancing interpretability and lowering processing costs.The optimized models further demonstrated perfect generalization,achieving 100%classification accuracy.These findings underscore the potential of advanced optimization techniques in developing efficient and accurate AD diagnostic tools suitable for environments with limited computational resources.
文摘Addressing the complex issue of emergency resource distribution center site selection in uncertain environments, this study was conducted to comprehensively consider factors such as uncertainty parameters and the urgency of demand at disaster-affected sites. Firstly, urgency cost, economic cost, and transportation distance cost were identified as key objectives. The study applied fuzzy theory integration to construct a triangular fuzzy multi-objective site selection decision model. Next, the defuzzification theory transformed the fuzzy decision model into a precise one. Subsequently, an improved Chaotic Quantum Multi-Objective Harris Hawks Optimization (CQ-MOHHO) algorithm was proposed to solve the model. The CQ-MOHHO algorithm was shown to rapidly produce high-quality Pareto front solutions and identify optimal site selection schemes for emergency resource distribution centers through case studies. This outcome verified the feasibility and efficacy of the site selection decision model and the CQ-MOHHO algorithm. To further assess CQ-MOHHO’s performance, Zitzler-Deb-Thiele (ZDT) test functions, commonly used in multi-objective optimization, were employed. Comparisons with Multi-Objective Harris Hawks Optimization (MOHHO), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Multi-Objective Grey Wolf Optimizer (MOGWO) using Generational Distance (GD), Hypervolume (HV), and Inverted Generational Distance (IGD) metrics showed that CQ-MOHHO achieved superior global search ability, faster convergence, and higher solution quality. The CQ-MOHHO algorithm efficiently achieved a balance between multiple objectives, providing decision-makers with satisfactory solutions and a valuable reference for researching and applying emergency site selection problems.
文摘Hybrid renewable energy systems(HRES)offer cost-effectiveness,low-emission power solutions,and reduced dependence on fossil fuels.However,the renewable energy allocation problem remains challenging due to complex system interactions and multiple operational constraints.This study develops a novel Multi-Neighborhood Enhanced Harris Hawks Optimization(MNEHHO)algorithm to address the allocation of HRES components.The proposed approach integrates key technical parameters,including charge-discharge efficiency,storage device configurations,and renewable energy fraction.We formulate a comprehensive mathematical model that simultaneously minimizes levelized energy costs and pollutant emissions while maintaining system reliability.The MNEHHO algorithm employs multiple neighborhood structures to enhance solution diversity and exploration capabilities.The model’s effectiveness is validated through case studies across four distinct institutional energy demand profiles.Results demonstrate that our approach successfully generates practically feasible HRES configurations while achieving significant reductions in costs and emissions compared to conventional methods.The enhanced search mechanisms of MNEHHO show superior performance in avoiding local optima and achieving consistent solutions.Experimental results demonstrate concrete improvements in solution quality(up to 46% improvement in objective value)and computational efficiency(average coefficient of variance of 24%-27%)across diverse institutional settings.This confirms the robustness and scalability of our method under various operational scenarios,providing a reliable framework for solving renewable energy allocation problems.
基金supported by the National Natural Science Foundation of China(No.62071365)the Key Research and Development Program of Shaanxi Province(No.2017ZDCXL-GY-06-02).
文摘To improve the quality of the illumination distribution,one novel indoor visible light communication(VLC)system,which is jointly assisted by the angle-diversity transceivers and simultaneous transmission and reflection-intelligent reflecting surface(STAR-IRS),has been proposed in this work.A Harris Hawks optimizer algorithm(HHOA)-based two-stage alternating iteration algorithm(TSAIA)is presented to jointly optimize the magnitude and uniformity of the received optical power.Besides,to demonstrate the superiority of the proposed strategy,several benchmark schemes are simulated and compared.Results showed that compared to other optimization strategies,the TSAIA scheme is more capable of balancing the average value and variance of the received optical power,when the maximal ratio combining(MRC)strategy is adopted at the receiver.Moreover,as the number of the STAR-IRS elements increases,the optical power variance of the system optimized by TSAIA scheme would become smaller while the average optical power would get larger.This study will benefit the design of received optical power distribution for indoor VLC systems.
