Quantum coherence constitutes a fundamental physical mechanism essential to the study of quantum algorithms.We study coherence and decoherence in the generalized Shor's algorithm where the register A is initialize...Quantum coherence constitutes a fundamental physical mechanism essential to the study of quantum algorithms.We study coherence and decoherence in the generalized Shor's algorithm where the register A is initialized in an arbitrary pure state,or the combined register AB is initialized in a pseudo-pure state,which encompasses the standard Shor's algorithm as a special case.We derive both lower and upper bounds on the performance of the generalized Shor's algorithm,and establish the relation between the probability of calculating the order r when register AB is initialized in a pseudo-pure state and that when register A is initialized in an arbitrary pure state.Moreover,we study coherence and decoherence in the noisy Shor's algorithm and give a lower bound on the probability that we can calculate the order r.展开更多
We introduce a new three-party semi-quantum dialogue(3P-SQD)protocol that combines GHZ-state-based semiquantum communication,a Grover's algorithm-driven 2-bit encoding scheme,and hypergraph-based access control.In...We introduce a new three-party semi-quantum dialogue(3P-SQD)protocol that combines GHZ-state-based semiquantum communication,a Grover's algorithm-driven 2-bit encoding scheme,and hypergraph-based access control.In each round,the fully quantum participant Alice sends two bits,whereas the semi-quantum participants Bob and Charlie,restricted to semi-quantum operations such as measurements in the computational basis and reflection,each transmit one bit.The protocol incorporates probe state checking,Grover's algorithm-based encoding,and hypergraph-based authorization.It achieves information-theoretic security and controlled access,while preserving high message throughput and imposing no additional requirements on the semi-quantum users.展开更多
As an important quantum cryptanalysis algorithm,Kuperberg's algorithm efficiently addresses the dihedral hidden subgroup problem with sub-exponential acceleration.However,when dealing with large numbers,the algori...As an important quantum cryptanalysis algorithm,Kuperberg's algorithm efficiently addresses the dihedral hidden subgroup problem with sub-exponential acceleration.However,when dealing with large numbers,the algorithm demands a deeper quantum circuit depth,rendering its implementation on current quantum devices prone to noise interference and thereby significantly reducing its efficiency.To mitigate this challenge,this paper proposes a distributed Kuperberg's algorithm.It decomposes the original function into sub-functions which can be executed on different nodes in parallel.The implementation of these sub-functions necessitates a shallower quantum circuit depth and a reduced number of qubits when contrasted with the execution of the original function.Furthermore,the proposed algorithm can be directly generalized to an arbitrary number of nodes by adjusting the quantity of input qubits.The utilization of multi-node parallel processing makes the proposed algorithm a linear enhancement in query complexity relative to the original algorithm.To validate the feasibility and demonstrate the superiority of our algorithm,experiments are conducted on the Qiskit platform.展开更多
Gas hydrates are increasingly recognized as a significant unconventional energy resource and a key factor in marine geohazards and the global carbon cycle.However,accurately identifying and quantifying hydrate-bearing...Gas hydrates are increasingly recognized as a significant unconventional energy resource and a key factor in marine geohazards and the global carbon cycle.However,accurately identifying and quantifying hydrate-bearing formations remains challenging due to complex geophysical signatures and heterogeneous distribution.This study evaluates twelve supervised machine learning(ML)algorithms for two key tasks:Classification of hydrate-bearing layers and regression-based estimation of hydrate saturation,using well log and pore-water geochemical data from Site NGHP-01-19B.Two physically independent labeling frameworks are employed:One based on Archie's law using resistivity(1350 samples,29%hydratebearing),and another based on a three-phase velocity model(890 samples,25%hydrate-bearing).A diverse set of models,including tree-based ensembles(Decision Tree,Random Forest,GBDT,XGBoost,Light GBM,Cat Boost,Bagging,Ada Boost),kernel methods(SVM,SVR),instance-based learning(KNN),neural networks(MLP),and Gaussian Process models(GPR,GPC),are systematically compared using cross-validation and grid search.Ensemble methods consistently performed best in classification,with Ada Boost and GBDT,achieving test accuracies above 0.94(Archie)and 0.98(velocity-based).For regression,GPR delivered the most accurate hydrate saturation estimates(R2>0.99),while GBDT and Random Forest provided a strong balance of accuracy and computational efficiency.Notably,depth below seafloor(TDEP),though not a direct geophysical input,significantly enhanced model performance by acting as a proxy for stratigraphic and thermodynamic conditions.Group-based validation confirmed that random-sample splitting overestimates performance due to depth-wise autocorrelation,highlighting the importance of geologically informed model assessment.Overall,the consistent performance of ML models across both labeling schemes and input feature sets underscores their robustness and transferability,supporting their use as a reliable toolset for offshore gas hydrate reservoir characterization.展开更多
In the rapidly evolving domain of quantum computing,Shor’s algorithm has emerged as a groundbreaking innovation with far-reaching implications for the field of cryptographic security.However,the efficacy of Shor’s a...In the rapidly evolving domain of quantum computing,Shor’s algorithm has emerged as a groundbreaking innovation with far-reaching implications for the field of cryptographic security.However,the efficacy of Shor’s algorithm hinges on the critical step of determining the period,a process that poses a substantial computational challenge.This article explores innovative quantum optimization solutions that aim to enhance the efficiency of Shor’s period finding algorithm.The article focuses on quantum development environments,such as Qiskit and Cirq.A detailed analysis is conducted on three notable tools:Qiskit Transpiler,BQSKit,and Mitiq.The performance of these tools is evaluated in terms of execution time,precision,resource utilization,the number of quantum gates,circuit synthesis optimization,error mitigation,and qubit fidelity.Through rigorous case studies,we highlight the strengths and limitations of these tools,shedding light on their potential impact on integer factorization and cybersecurity.Our findings underscore the importance of quantum optimization and lay the foundation for future developments in quantum algorithmic enhancements,particularly within the Qiskit and Cirq quantum development environments.展开更多
The modeling and optimization of wind farm layouts can effectively reduce the wake effect between turbine units,thereby enhancing the expected output power and avoiding negative influence.Traditional wind farm optimiz...The modeling and optimization of wind farm layouts can effectively reduce the wake effect between turbine units,thereby enhancing the expected output power and avoiding negative influence.Traditional wind farm optimization often uses idealized wake models,neglecting the influence of wind shear at different elevations,which leads to a lack of precision in estimating wake effects and fails to meet the accuracy and reliability requirements of practical engineering.To address this,we have constructed a three-dimensional 3D wind farm optimization model that incorporates elevation,utilizing a 3D wake model to better reflect real-world conditions.We aim to assess the optimization state of the algorithm and provide strong incentives at the right moments to ensure continuous evolution of the population.To this end,we propose an evolutionary adaptation degreeguided genetic algorithm based on power-law perturbation(PPGA)to adapt multidimensional conditions.We select the offshore wind power project in Nantong,Jiangsu,China,as a study example and compare PPGA with other well-performing algorithms under this practical project.Based on the actual wind condition data,the experimental results demonstrate that PPGA can effectively tackle this complex problem and achieve the best power efficiency.展开更多
The computational accuracy and efficiency of modeling the stress spectrum derived from bridge monitoring data significantly influence the fatigue life assessment of steel bridges.Therefore,determining the optimal stre...The computational accuracy and efficiency of modeling the stress spectrum derived from bridge monitoring data significantly influence the fatigue life assessment of steel bridges.Therefore,determining the optimal stress spectrum model is crucial for further fatigue reliability analysis.This study investigates the performance of the REBMIX algorithm in modeling both univariate(stress range)and multivariate(stress range and mean stress)distributions of the rain-flowmatrix for a steel arch bridge,usingAkaike’s Information Criterion(AIC)as a performance metric.Four types of finitemixture distributions—Normal,Lognormal,Weibull,and Gamma—are employed tomodel the stress range.Additionally,mixed distributions,including Normal-Normal,Lognormal-Normal,Weibull-Normal,and Gamma-Normal,are utilized to model the joint distribution of stress range and mean stress.The REBMIX algorithm estimates the number of components,component weights,and component parameters for each candidate finite mixture distribution.The results demonstrate that the REBMIX algorithm-based mixture parameter estimation approach effectively identifies the optimal distribution based on AIC values.Furthermore,the algorithm exhibits superior computational efficiency compared to traditional methods,making it highly suitable for practical applications.展开更多
Shor proposed a polynomial time algorithm for computing the order of one element in a multiplicative group using a quantum computer. Based on Miller’s randomization, he then gave a factorization algorithm. But the al...Shor proposed a polynomial time algorithm for computing the order of one element in a multiplicative group using a quantum computer. Based on Miller’s randomization, he then gave a factorization algorithm. But the algorithm has two shortcomings, the order must be even and the output might be a trivial factor. Actually, these drawbacks can be overcome if the number is an RSA modulus. Applying the special structure of the RSA modulus, an algorithm is presented to overcome the two shortcomings. The new algorithm improves Shor’s algorithm for factoring RSA modulus. The cost of the factorization algorithm almost depends on the calculation of the order of 2 in the multiplication group.展开更多
It is widely believed that Shor's factoring algorithm provides a driving force to boost the quantum computing research.However, a serious obstacle to its binary implementation is the large number of quantum gates. No...It is widely believed that Shor's factoring algorithm provides a driving force to boost the quantum computing research.However, a serious obstacle to its binary implementation is the large number of quantum gates. Non-binary quantum computing is an efficient way to reduce the required number of elemental gates. Here, we propose optimization schemes for Shor's algorithm implementation and take a ternary version for factorizing 21 as an example. The optimized factorization is achieved by a two-qutrit quantum circuit, which consists of only two single qutrit gates and one ternary controlled-NOT gate. This two-qutrit quantum circuit is then encoded into the nine lower vibrational states of an ion trapped in a weakly anharmonic potential. Optimal control theory(OCT) is employed to derive the manipulation electric field for transferring the encoded states. The ternary Shor's algorithm can be implemented in one single step. Numerical simulation results show that the accuracy of the state transformations is about 0.9919.展开更多
针对分布式光伏中常见的最大功率点跟踪(maximum power point tracking,MPPT)算法难以同时解决组件本身失配与组件间失配的问题,提出一种轻量级MPPT算法——搜寻维持法(search and maintain,S&M)。该算法在搜寻阶段通过一次遍历快...针对分布式光伏中常见的最大功率点跟踪(maximum power point tracking,MPPT)算法难以同时解决组件本身失配与组件间失配的问题,提出一种轻量级MPPT算法——搜寻维持法(search and maintain,S&M)。该算法在搜寻阶段通过一次遍历快速获取光伏组件的功率,并在确定全局最大功率点后切换到维持阶段;在维持阶段,通过PID微调,使工作点稳定运行在最大功率点附近。该算法计算量小,便于在各类嵌入式平台中实现。Simulink仿真结果表明,S&M算法相较于扰动观察法(perturb and observe,P&O)能有效缓解组件本身失配下的多峰值问题,相较于粒子群优化(particle swarm optimization,PSO)算法能够抑制组件间失配时多组件并行MPPT所引起的相互干扰。其全局最大功率点跟踪成功率达到99%以上,收敛时间可缩短至0.01 s;通过实物验证进一步证明了该算法在实际系统中能够实现多组件协同的全局最大功率点跟踪。展开更多
This study is trying to address the critical need for efficient routing in Mobile Ad Hoc Networks(MANETs)from dynamic topologies that pose great challenges because of the mobility of nodes.Themain objective was to del...This study is trying to address the critical need for efficient routing in Mobile Ad Hoc Networks(MANETs)from dynamic topologies that pose great challenges because of the mobility of nodes.Themain objective was to delve into and refine the application of the Dijkstra’s algorithm in this context,a method conventionally esteemed for its efficiency in static networks.Thus,this paper has carried out a comparative theoretical analysis with the Bellman-Ford algorithm,considering adaptation to the dynamic network conditions that are typical for MANETs.This paper has shown through detailed algorithmic analysis that Dijkstra’s algorithm,when adapted for dynamic updates,yields a very workable solution to the problem of real-time routing in MANETs.The results indicate that with these changes,Dijkstra’s algorithm performs much better computationally and 30%better in routing optimization than Bellman-Ford when working with configurations of sparse networks.The theoretical framework adapted,with the adaptation of the Dijkstra’s algorithm for dynamically changing network topologies,is novel in this work and quite different from any traditional application.The adaptation should offer more efficient routing and less computational overhead,most apt in the limited resource environment of MANETs.Thus,from these findings,one may derive a conclusion that the proposed version of Dijkstra’s algorithm is the best and most feasible choice of the routing protocol for MANETs given all pertinent key performance and resource consumption indicators and further that the proposed method offers a marked improvement over traditional methods.This paper,therefore,operationalizes the theoretical model into practical scenarios and also further research with empirical simulations to understand more about its operational effectiveness.展开更多
Despite deep learning’s high precision in emotion identification,centralized training is associated with privacy and scalability concerns.The privacy-preserving federated learning model,Federated Hybrid-Optimized Emo...Despite deep learning’s high precision in emotion identification,centralized training is associated with privacy and scalability concerns.The privacy-preserving federated learning model,Federated Hybrid-Optimized Emotion Recognition(Fed-HOER),introduced in this paper is an auto-tuning hyperparameters optimizer based on a hybrid Dung Beetle Optimizer-Fick’s Law Algorithm(DBO-FLA)optimizer.The global and local searches are optimized at two levels,and validation loss is minimized by 22%–24%without sharing raw data.The experiments on Extended Cohn–Kanade(CK+),Japanese Female Facial Expressions(JAFFE),and Karolinska Directed Emotional Faces(KDEF)exhibit a high generalization rate with a mean accuracy of 98.14.The findings demonstrate that Fed-HOER is statistically significantly better than baseline configurations.The results show that the suggested framework offers a favorable trade-off between predictive accuracy and privacy protection,which is why it can be used in the healthcare,educational,and other emotion-related fields.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.12561084 and 12161056)the Natural Science Foundation of Jiangxi Province,China(Grant No.20232ACB211003)。
摘要Quantum coherence constitutes a fundamental physical mechanism essential to the study of quantum algorithms.We study coherence and decoherence in the generalized Shor's algorithm where the register A is initialized in an arbitrary pure state,or the combined register AB is initialized in a pseudo-pure state,which encompasses the standard Shor's algorithm as a special case.We derive both lower and upper bounds on the performance of the generalized Shor's algorithm,and establish the relation between the probability of calculating the order r when register AB is initialized in a pseudo-pure state and that when register A is initialized in an arbitrary pure state.Moreover,we study coherence and decoherence in the noisy Shor's algorithm and give a lower bound on the probability that we can calculate the order r.
基金supported by the National Natural Science Foundation of China(Grant No.12201300)the Nanjing University of Science and Technology Undergraduate Innovation Training Program(Grant No.S202510288017)。
摘要We introduce a new three-party semi-quantum dialogue(3P-SQD)protocol that combines GHZ-state-based semiquantum communication,a Grover's algorithm-driven 2-bit encoding scheme,and hypergraph-based access control.In each round,the fully quantum participant Alice sends two bits,whereas the semi-quantum participants Bob and Charlie,restricted to semi-quantum operations such as measurements in the computational basis and reflection,each transmit one bit.The protocol incorporates probe state checking,Grover's algorithm-based encoding,and hypergraph-based authorization.It achieves information-theoretic security and controlled access,while preserving high message throughput and imposing no additional requirements on the semi-quantum users.
基金Project supported by the National Natural Science Foundation of China(Grant No.62171131)the Natural Science Foundation of Fujian Province,China(Grant Nos.2022J01186 and 2023J01533)the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0302901)。
摘要As an important quantum cryptanalysis algorithm,Kuperberg's algorithm efficiently addresses the dihedral hidden subgroup problem with sub-exponential acceleration.However,when dealing with large numbers,the algorithm demands a deeper quantum circuit depth,rendering its implementation on current quantum devices prone to noise interference and thereby significantly reducing its efficiency.To mitigate this challenge,this paper proposes a distributed Kuperberg's algorithm.It decomposes the original function into sub-functions which can be executed on different nodes in parallel.The implementation of these sub-functions necessitates a shallower quantum circuit depth and a reduced number of qubits when contrasted with the execution of the original function.Furthermore,the proposed algorithm can be directly generalized to an arbitrary number of nodes by adjusting the quantity of input qubits.The utilization of multi-node parallel processing makes the proposed algorithm a linear enhancement in query complexity relative to the original algorithm.To validate the feasibility and demonstrate the superiority of our algorithm,experiments are conducted on the Qiskit platform.
基金supported by the China Scholarship Council under the State Scholarship Fund(202506340082)the Key Project of Guangdong Provincial Key R&D Program(2023B1111050014)+3 种基金the Youth Promotion Project of the Natural Science Foundation of Guangdong Province(2023A1515030280)the Guangdong Basic and Applied Basic Research Foundation(2023A1515010926)the Guangzhou Science and Technology Plan Project(2024A04J9876)funded by China National Petroleum Corporation(CNPC,2024DQ02-0107)。
摘要Gas hydrates are increasingly recognized as a significant unconventional energy resource and a key factor in marine geohazards and the global carbon cycle.However,accurately identifying and quantifying hydrate-bearing formations remains challenging due to complex geophysical signatures and heterogeneous distribution.This study evaluates twelve supervised machine learning(ML)algorithms for two key tasks:Classification of hydrate-bearing layers and regression-based estimation of hydrate saturation,using well log and pore-water geochemical data from Site NGHP-01-19B.Two physically independent labeling frameworks are employed:One based on Archie's law using resistivity(1350 samples,29%hydratebearing),and another based on a three-phase velocity model(890 samples,25%hydrate-bearing).A diverse set of models,including tree-based ensembles(Decision Tree,Random Forest,GBDT,XGBoost,Light GBM,Cat Boost,Bagging,Ada Boost),kernel methods(SVM,SVR),instance-based learning(KNN),neural networks(MLP),and Gaussian Process models(GPR,GPC),are systematically compared using cross-validation and grid search.Ensemble methods consistently performed best in classification,with Ada Boost and GBDT,achieving test accuracies above 0.94(Archie)and 0.98(velocity-based).For regression,GPR delivered the most accurate hydrate saturation estimates(R2>0.99),while GBDT and Random Forest provided a strong balance of accuracy and computational efficiency.Notably,depth below seafloor(TDEP),though not a direct geophysical input,significantly enhanced model performance by acting as a proxy for stratigraphic and thermodynamic conditions.Group-based validation confirmed that random-sample splitting overestimates performance due to depth-wise autocorrelation,highlighting the importance of geologically informed model assessment.Overall,the consistent performance of ML models across both labeling schemes and input feature sets underscores their robustness and transferability,supporting their use as a reliable toolset for offshore gas hydrate reservoir characterization.
摘要In the rapidly evolving domain of quantum computing,Shor’s algorithm has emerged as a groundbreaking innovation with far-reaching implications for the field of cryptographic security.However,the efficacy of Shor’s algorithm hinges on the critical step of determining the period,a process that poses a substantial computational challenge.This article explores innovative quantum optimization solutions that aim to enhance the efficiency of Shor’s period finding algorithm.The article focuses on quantum development environments,such as Qiskit and Cirq.A detailed analysis is conducted on three notable tools:Qiskit Transpiler,BQSKit,and Mitiq.The performance of these tools is evaluated in terms of execution time,precision,resource utilization,the number of quantum gates,circuit synthesis optimization,error mitigation,and qubit fidelity.Through rigorous case studies,we highlight the strengths and limitations of these tools,shedding light on their potential impact on integer factorization and cybersecurity.Our findings underscore the importance of quantum optimization and lay the foundation for future developments in quantum algorithmic enhancements,particularly within the Qiskit and Cirq quantum development environments.
基金partially supported by the Japan Society for the Promotion of Science(JSPS)KAKENHI(JP23K24899)Japan Science and Technology Agency(JST)Support for Pioneering Research Initiated by the Next Generation(SPRING)(JPMJSP2145).
摘要The modeling and optimization of wind farm layouts can effectively reduce the wake effect between turbine units,thereby enhancing the expected output power and avoiding negative influence.Traditional wind farm optimization often uses idealized wake models,neglecting the influence of wind shear at different elevations,which leads to a lack of precision in estimating wake effects and fails to meet the accuracy and reliability requirements of practical engineering.To address this,we have constructed a three-dimensional 3D wind farm optimization model that incorporates elevation,utilizing a 3D wake model to better reflect real-world conditions.We aim to assess the optimization state of the algorithm and provide strong incentives at the right moments to ensure continuous evolution of the population.To this end,we propose an evolutionary adaptation degreeguided genetic algorithm based on power-law perturbation(PPGA)to adapt multidimensional conditions.We select the offshore wind power project in Nantong,Jiangsu,China,as a study example and compare PPGA with other well-performing algorithms under this practical project.Based on the actual wind condition data,the experimental results demonstrate that PPGA can effectively tackle this complex problem and achieve the best power efficiency.
基金jointly supported by the Fundamental Research Funds for the Central Universities(Grant No.xzy012023075)the Zhejiang Engineering Research Center of Intelligent Urban Infrastructure(Grant No.IUI2023-YB-12).
摘要The computational accuracy and efficiency of modeling the stress spectrum derived from bridge monitoring data significantly influence the fatigue life assessment of steel bridges.Therefore,determining the optimal stress spectrum model is crucial for further fatigue reliability analysis.This study investigates the performance of the REBMIX algorithm in modeling both univariate(stress range)and multivariate(stress range and mean stress)distributions of the rain-flowmatrix for a steel arch bridge,usingAkaike’s Information Criterion(AIC)as a performance metric.Four types of finitemixture distributions—Normal,Lognormal,Weibull,and Gamma—are employed tomodel the stress range.Additionally,mixed distributions,including Normal-Normal,Lognormal-Normal,Weibull-Normal,and Gamma-Normal,are utilized to model the joint distribution of stress range and mean stress.The REBMIX algorithm estimates the number of components,component weights,and component parameters for each candidate finite mixture distribution.The results demonstrate that the REBMIX algorithm-based mixture parameter estimation approach effectively identifies the optimal distribution based on AIC values.Furthermore,the algorithm exhibits superior computational efficiency compared to traditional methods,making it highly suitable for practical applications.
摘要Shor proposed a polynomial time algorithm for computing the order of one element in a multiplicative group using a quantum computer. Based on Miller’s randomization, he then gave a factorization algorithm. But the algorithm has two shortcomings, the order must be even and the output might be a trivial factor. Actually, these drawbacks can be overcome if the number is an RSA modulus. Applying the special structure of the RSA modulus, an algorithm is presented to overcome the two shortcomings. The new algorithm improves Shor’s algorithm for factoring RSA modulus. The cost of the factorization algorithm almost depends on the calculation of the order of 2 in the multiplication group.
基金supported by the National Natural Science Foundation of China(Grant No.61205108)the High Performance Computing(HPC)Foundation of National University of Defense Technology,China
摘要It is widely believed that Shor's factoring algorithm provides a driving force to boost the quantum computing research.However, a serious obstacle to its binary implementation is the large number of quantum gates. Non-binary quantum computing is an efficient way to reduce the required number of elemental gates. Here, we propose optimization schemes for Shor's algorithm implementation and take a ternary version for factorizing 21 as an example. The optimized factorization is achieved by a two-qutrit quantum circuit, which consists of only two single qutrit gates and one ternary controlled-NOT gate. This two-qutrit quantum circuit is then encoded into the nine lower vibrational states of an ion trapped in a weakly anharmonic potential. Optimal control theory(OCT) is employed to derive the manipulation electric field for transferring the encoded states. The ternary Shor's algorithm can be implemented in one single step. Numerical simulation results show that the accuracy of the state transformations is about 0.9919.
摘要针对分布式光伏中常见的最大功率点跟踪(maximum power point tracking,MPPT)算法难以同时解决组件本身失配与组件间失配的问题,提出一种轻量级MPPT算法——搜寻维持法(search and maintain,S&M)。该算法在搜寻阶段通过一次遍历快速获取光伏组件的功率,并在确定全局最大功率点后切换到维持阶段;在维持阶段,通过PID微调,使工作点稳定运行在最大功率点附近。该算法计算量小,便于在各类嵌入式平台中实现。Simulink仿真结果表明,S&M算法相较于扰动观察法(perturb and observe,P&O)能有效缓解组件本身失配下的多峰值问题,相较于粒子群优化(particle swarm optimization,PSO)算法能够抑制组件间失配时多组件并行MPPT所引起的相互干扰。其全局最大功率点跟踪成功率达到99%以上,收敛时间可缩短至0.01 s;通过实物验证进一步证明了该算法在实际系统中能够实现多组件协同的全局最大功率点跟踪。
基金supported by Northern Border University,Arar,Kingdom of Saudi Arabia,through the Project Number“NBU-FFR-2024-2248-03”.
摘要This study is trying to address the critical need for efficient routing in Mobile Ad Hoc Networks(MANETs)from dynamic topologies that pose great challenges because of the mobility of nodes.Themain objective was to delve into and refine the application of the Dijkstra’s algorithm in this context,a method conventionally esteemed for its efficiency in static networks.Thus,this paper has carried out a comparative theoretical analysis with the Bellman-Ford algorithm,considering adaptation to the dynamic network conditions that are typical for MANETs.This paper has shown through detailed algorithmic analysis that Dijkstra’s algorithm,when adapted for dynamic updates,yields a very workable solution to the problem of real-time routing in MANETs.The results indicate that with these changes,Dijkstra’s algorithm performs much better computationally and 30%better in routing optimization than Bellman-Ford when working with configurations of sparse networks.The theoretical framework adapted,with the adaptation of the Dijkstra’s algorithm for dynamically changing network topologies,is novel in this work and quite different from any traditional application.The adaptation should offer more efficient routing and less computational overhead,most apt in the limited resource environment of MANETs.Thus,from these findings,one may derive a conclusion that the proposed version of Dijkstra’s algorithm is the best and most feasible choice of the routing protocol for MANETs given all pertinent key performance and resource consumption indicators and further that the proposed method offers a marked improvement over traditional methods.This paper,therefore,operationalizes the theoretical model into practical scenarios and also further research with empirical simulations to understand more about its operational effectiveness.
摘要Despite deep learning’s high precision in emotion identification,centralized training is associated with privacy and scalability concerns.The privacy-preserving federated learning model,Federated Hybrid-Optimized Emotion Recognition(Fed-HOER),introduced in this paper is an auto-tuning hyperparameters optimizer based on a hybrid Dung Beetle Optimizer-Fick’s Law Algorithm(DBO-FLA)optimizer.The global and local searches are optimized at two levels,and validation loss is minimized by 22%–24%without sharing raw data.The experiments on Extended Cohn–Kanade(CK+),Japanese Female Facial Expressions(JAFFE),and Karolinska Directed Emotional Faces(KDEF)exhibit a high generalization rate with a mean accuracy of 98.14.The findings demonstrate that Fed-HOER is statistically significantly better than baseline configurations.The results show that the suggested framework offers a favorable trade-off between predictive accuracy and privacy protection,which is why it can be used in the healthcare,educational,and other emotion-related fields.