Data serves as the foundation for training and testing machine learning and artificial intelligencemodels.The most fundamental part of data is its attributes or features.The feature set size changes from one dataset t...Data serves as the foundation for training and testing machine learning and artificial intelligencemodels.The most fundamental part of data is its attributes or features.The feature set size changes from one dataset to another.Only the relevant features contributemeaningfully to classificationaccuracy.The presence of irrelevant features reduces the system’s effectiveness.Classification performance often deteriorates on high-dimensional datasets due to the large search space.Thus,one of the significant obstacles affecting the performance of the learning process in the majority of machine learning and data mining techniques is the dimensionality of the datasets.Feature selection(FS)is an effective preprocessing step in classification tasks.The aim of applying FS is to exclude redundant and unrelated features while retaining the most informative ones to optimize classification capability and compress computational complexity.In this paper,a novel hybrid binary metaheuristic algorithm,termed hSC-FPA,is proposed by hybridizing the Flower Pollination Algorithm(FPA)and the Sine Cosine Algorithm(SCA).Hybridization controls the exploration capacity of SCA and the exploitation behavior of FPA to maintain a balanced search process.SCA guides the global search in the early iterations,while FPA’s local pollination refines promising solutions in later stages.A binary conversion mechanism using a threshold function is implemented to handle the discrete nature of the feature selection problem.The functionality of the proposed hSC-FPA is authenticated on fourteen standard datasets from the UCI repository using the K-Nearest Neighbors(K-NN)classifier.Experimental results are benchmarked against the standalone SCA and FPA algorithms.The hSC-FPA consistently achieves higher classification accuracy,selects a more compact feature subset,and demonstrates superior convergence behavior.These findings support the stability and outperformance of the hybrid feature selection method presented.展开更多
The sine oculis homeobox homolog(SIX)family,a group of transcription factors characterized by a conserved DNA-binding homology domain,plays a critical role in orchestrating embryonic development and organogenesis acro...The sine oculis homeobox homolog(SIX)family,a group of transcription factors characterized by a conserved DNA-binding homology domain,plays a critical role in orchestrating embryonic development and organogenesis across various organisms,including humans.Comprising six distinct members,from SIX1 to SIX6,each member contributes uniquely to the development and differentiation of diverse tissues and organs,underscoring the versatility of the SIX family.Dysregulation or mutations in SIX genes have been implicated in a spectrum of developmental disorders,as well as in tumor initiation and progression,highlighting their pivotal role in maintaining normal developmental trajectories and cellular functions.Efforts to target the transcriptional complex of the SIX gene family have emerged as a promising strategy to inhibit tumor development.While the development of inhibitors targeting this gene family is still in its early stages,the significant potential of such interventions holds promise for future therapeutic advances.Therefore,this review aimed to comprehensively explore the advancements in understanding the SIX family within gastrointestinal cancers,focusing on its critical role in normal organ development and its implications in gastrointestinal cancers,including gastric,pancreatic,colorectal cancer,and hepatocellular carcinomas.In conclusion,this review deepened the understanding of the functional roles of the SIX family and explored the potential of utilizing this gene family for the diagnosis,prognosis,and treatment of gastrointestinal cancers.展开更多
阐述了昆虫物候分析的SSPM模型(single sine phonological model)计算方程和过程,该模型采用Sine函数拟合每天温度变化,并利用积分,获取每天有效积温和一定时间内日度累积值。SSPM重要的参数有发育起点温度和上限温度,参与模型计算包括...阐述了昆虫物候分析的SSPM模型(single sine phonological model)计算方程和过程,该模型采用Sine函数拟合每天温度变化,并利用积分,获取每天有效积温和一定时间内日度累积值。SSPM重要的参数有发育起点温度和上限温度,参与模型计算包括日最高气温和日最低气温2个输入值,在输入值和参数值组合条件下,模型有6个不同计算方程。以棉铃虫(Helicoverpa armigera)为例,介绍了SSPM在昆虫发育历期分析过程,简单表述了模型预测功能。随自动化气象站分布密度增加和Internet技术的发展,模型在区域化害虫管理中有着重要的应用前景。展开更多
Data security has become a growing priority due to the increasing frequency of cyber-attacks,necessitating the development of more advanced encryption algorithms.This paper introduces Single Qubit Quantum Logistic-Sin...Data security has become a growing priority due to the increasing frequency of cyber-attacks,necessitating the development of more advanced encryption algorithms.This paper introduces Single Qubit Quantum Logistic-Sine XYZ-Rotation Maps(SQQLSR),a quantum-based chaos map designed to generate one-dimensional chaotic sequences with an ultra-wide parameter range.The proposed model leverages quantum superposition using Hadamard gates and quantum rotations along the X,Y,and Z axes to enhance randomness.Extensive numerical experiments validate the effectiveness of SQQLSR.The proposed method achieves a maximum Lyapunov exponent(LE)of≈55.265,surpassing traditional chaotic maps in unpredictability.The bifurcation analysis confirms a uniform chaotic distribution,eliminating periodic windows and ensuring higher randomness.The system also generates an expanded key space exceeding 10^(40),enhancing security against brute-force attacks.Additionally,SQQLSR is applied to image encryption using a simple three-layer encryption scheme combining permutation and substitution techniques.This approach is intentionally designed to highlight the impact of SQQLSR-generated chaotic sequences rather than relying on a complex encryption algorithm.Theencryption method achieves an average entropy of 7.9994,NPCR above 99.6%,and UACI within 32.8%–33.8%,confirming its strong randomness and sensitivity to minor modifications.The robustness tests against noise,cropping,and JPEG compression demonstrate its resistance to statistical and differential attacks.Additionally,the decryption process ensures perfect image reconstruction with an infinite PSNR value,proving the algorithm’s reliability.These results highlight SQQLSR’s potential as a lightweight yet highly secure encryption mechanism suitable for quantum cryptography and secure communications.展开更多
The WSN(wireless sensor network)node optimization problem faces the challenge of efficient deployment and adaptation under limited resources and a dynamically changing environment.The complex and changing deployment e...The WSN(wireless sensor network)node optimization problem faces the challenge of efficient deployment and adaptation under limited resources and a dynamically changing environment.The complex and changing deployment environment puts higher requirements on the search space,computational cost,and optimization efficiency of the algorithms.For this reason,a slime mould algorithm called SCA-SMA is proposed to solve the above problem.In SCA-SMA,a reverse Sobol sequence is used to initialize the population to increase the population diversity and improve the probability of approaching the optimal solution.To better balance local exploitation and global exploration,a dynamic selection of sine cosine update mechanism is proposed:using an optimal position selection mechanism in the global exploration phase to avoid local optima,and integrating the sine cosine algorithm in the local exploitation phase to improve the mucilage position update method,enrich the optimization search process and enhance the development capability of the algorithm.Finally,an adaptive mutation strategy can be proposed to increase the search range of the algorithm and motivate SCA-SMA to explore more promising regions.To evaluate the performance of the algorithm,SCA-SMA is experimentally validated in five different aspects.The results show that SCA-SMA is significantly competitive compared to advanced MAs.In particular,in facing the WSN node coverage problem,SCA-SMA has more obvious advantages in both average coverage and optimal coverage,which makes it possible to fully utilize the sensing range of each sensor node,while avoiding the waste of resources and the generation of monitoring blind zones.展开更多
The Sine and Wormhole Energy Whale Optimization Algorithm(SWEWOA)represents an advanced solution method for resolving Optimal Power Flow(OPF)problems in power systems equipped with Flexible AC Transmission System(FACT...The Sine and Wormhole Energy Whale Optimization Algorithm(SWEWOA)represents an advanced solution method for resolving Optimal Power Flow(OPF)problems in power systems equipped with Flexible AC Transmission System(FACTS)devices which include Thyristor-Controlled Series Compensator(TCSC),Thyristor-Controlled Phase Shifter(TCPS),and Static Var Compensator(SVC).SWEWOA expands Whale Optimization Algorithm(WOA)through the integration of sine and wormhole energy features thus improving exploration and exploitation capabilities for efficient convergence in complex non-linear OPF problems.A performance evaluation of SWEWOA takes place on the IEEE-30 bus test system through static and dynamic loading scenarios where it demonstrates better results than five contemporary algorithms:Adaptive Chaotic WOA(ACWOA),WOA,Chaotic WOA(CWOA),Sine Cosine Algorithm Differential Evolution(SCADE),and Hybrid Grey Wolf Optimization(HGWO).The research shows that SWEWOA delivers superior generation cost reduction than other algorithms by reaching a minimum of 0.9%better performance.SWEWOA demonstrates superior power loss performance by achieving(P_(loss,min))at the lowest level compared to all other tested algorithms which leads to better system energy efficiency.The dynamic loading performance of SWEWOA leads to a 4.38%reduction in gross costs which proves its capability to handle different operating conditions.The algorithm achieves top performance in Friedman Rank Test(FRT)assessments through multiple performance metrics which verifies its consistent reliability and strong stability during changing power demands.The repeated simulations show that SWEWOA generates mean costs(C_(gen,min))and mean power loss values(P_(loss,min))with small deviations which indicate its capability to maintain cost-effective solutions in each simulation run.SWEWOA demonstrates great potential as an advanced optimization solution for power system operations through the results presented in this study.展开更多
Active damping(AD)strategy is an economical and efficient method to solve the resonant problem of the permanent magnet synchronous motor(PMSM)drive system with inductor-capacitor(LC)sine wave filter.In this article,th...Active damping(AD)strategy is an economical and efficient method to solve the resonant problem of the permanent magnet synchronous motor(PMSM)drive system with inductor-capacitor(LC)sine wave filter.In this article,the AD methods used in PMSM drive system are classified as inherent damping(ID),state variable feedback,and digital filter.Based on this,the purpose of this article is to provide an overview and analysis of the AD methods on PMSM drive system in recent years,and to comprehensively review,compare,and summarize the stability,dynamic performance,robustness,and algorithm complexity.Furthermore,a new expansion of AD method based on capacitor current feedback with high-pass filter(HPF-CCF)is studied to ensure the effectiveness when the resonant frequency is around sixth of the sampling frequency.The simulation and experimental results validate the effectiveness of theoretical analysis.展开更多
基金supported by a research grant from Lahore College for Women University(LCWU),Lahore,Pakistan.
文摘Data serves as the foundation for training and testing machine learning and artificial intelligencemodels.The most fundamental part of data is its attributes or features.The feature set size changes from one dataset to another.Only the relevant features contributemeaningfully to classificationaccuracy.The presence of irrelevant features reduces the system’s effectiveness.Classification performance often deteriorates on high-dimensional datasets due to the large search space.Thus,one of the significant obstacles affecting the performance of the learning process in the majority of machine learning and data mining techniques is the dimensionality of the datasets.Feature selection(FS)is an effective preprocessing step in classification tasks.The aim of applying FS is to exclude redundant and unrelated features while retaining the most informative ones to optimize classification capability and compress computational complexity.In this paper,a novel hybrid binary metaheuristic algorithm,termed hSC-FPA,is proposed by hybridizing the Flower Pollination Algorithm(FPA)and the Sine Cosine Algorithm(SCA).Hybridization controls the exploration capacity of SCA and the exploitation behavior of FPA to maintain a balanced search process.SCA guides the global search in the early iterations,while FPA’s local pollination refines promising solutions in later stages.A binary conversion mechanism using a threshold function is implemented to handle the discrete nature of the feature selection problem.The functionality of the proposed hSC-FPA is authenticated on fourteen standard datasets from the UCI repository using the K-Nearest Neighbors(K-NN)classifier.Experimental results are benchmarked against the standalone SCA and FPA algorithms.The hSC-FPA consistently achieves higher classification accuracy,selects a more compact feature subset,and demonstrates superior convergence behavior.These findings support the stability and outperformance of the hybrid feature selection method presented.
基金Supported by the National Natural Science Foundation of China,No.82273457the Natural Science Foundation of Guangdong Province,No.2023A1515012762 and No.2021A1515010846+1 种基金Special Grant for Key Area Programs of Guangdong Department of Education,No.2021ZDZX2040Science and Technology Special Project of Guangdong Province,No.210715216902829.
文摘The sine oculis homeobox homolog(SIX)family,a group of transcription factors characterized by a conserved DNA-binding homology domain,plays a critical role in orchestrating embryonic development and organogenesis across various organisms,including humans.Comprising six distinct members,from SIX1 to SIX6,each member contributes uniquely to the development and differentiation of diverse tissues and organs,underscoring the versatility of the SIX family.Dysregulation or mutations in SIX genes have been implicated in a spectrum of developmental disorders,as well as in tumor initiation and progression,highlighting their pivotal role in maintaining normal developmental trajectories and cellular functions.Efforts to target the transcriptional complex of the SIX gene family have emerged as a promising strategy to inhibit tumor development.While the development of inhibitors targeting this gene family is still in its early stages,the significant potential of such interventions holds promise for future therapeutic advances.Therefore,this review aimed to comprehensively explore the advancements in understanding the SIX family within gastrointestinal cancers,focusing on its critical role in normal organ development and its implications in gastrointestinal cancers,including gastric,pancreatic,colorectal cancer,and hepatocellular carcinomas.In conclusion,this review deepened the understanding of the functional roles of the SIX family and explored the potential of utilizing this gene family for the diagnosis,prognosis,and treatment of gastrointestinal cancers.
文摘阐述了昆虫物候分析的SSPM模型(single sine phonological model)计算方程和过程,该模型采用Sine函数拟合每天温度变化,并利用积分,获取每天有效积温和一定时间内日度累积值。SSPM重要的参数有发育起点温度和上限温度,参与模型计算包括日最高气温和日最低气温2个输入值,在输入值和参数值组合条件下,模型有6个不同计算方程。以棉铃虫(Helicoverpa armigera)为例,介绍了SSPM在昆虫发育历期分析过程,简单表述了模型预测功能。随自动化气象站分布密度增加和Internet技术的发展,模型在区域化害虫管理中有着重要的应用前景。
基金funded by Kementerian Pendidikan Tinggi,Sains,dan Teknologi(Kemdiktisaintek),Indonesia,grant numbers 108/E5/PG.02.00.PL/2024,027/LL6/PB/AL.04/2024,061/A.38-04/UDN-09/VI/2024.
文摘Data security has become a growing priority due to the increasing frequency of cyber-attacks,necessitating the development of more advanced encryption algorithms.This paper introduces Single Qubit Quantum Logistic-Sine XYZ-Rotation Maps(SQQLSR),a quantum-based chaos map designed to generate one-dimensional chaotic sequences with an ultra-wide parameter range.The proposed model leverages quantum superposition using Hadamard gates and quantum rotations along the X,Y,and Z axes to enhance randomness.Extensive numerical experiments validate the effectiveness of SQQLSR.The proposed method achieves a maximum Lyapunov exponent(LE)of≈55.265,surpassing traditional chaotic maps in unpredictability.The bifurcation analysis confirms a uniform chaotic distribution,eliminating periodic windows and ensuring higher randomness.The system also generates an expanded key space exceeding 10^(40),enhancing security against brute-force attacks.Additionally,SQQLSR is applied to image encryption using a simple three-layer encryption scheme combining permutation and substitution techniques.This approach is intentionally designed to highlight the impact of SQQLSR-generated chaotic sequences rather than relying on a complex encryption algorithm.Theencryption method achieves an average entropy of 7.9994,NPCR above 99.6%,and UACI within 32.8%–33.8%,confirming its strong randomness and sensitivity to minor modifications.The robustness tests against noise,cropping,and JPEG compression demonstrate its resistance to statistical and differential attacks.Additionally,the decryption process ensures perfect image reconstruction with an infinite PSNR value,proving the algorithm’s reliability.These results highlight SQQLSR’s potential as a lightweight yet highly secure encryption mechanism suitable for quantum cryptography and secure communications.
基金supported by special project of the National Natural Science Foundation of China[No.42027806]special Fund of the National Natural Science Foundation of China[No.42041006]+3 种基金the National Key Research and Development Program Project of China[No.2018YFC1504705]the Key Program of the National Natural Science Foundation of China[No.61731015]the major instrument,the project of Natural Science Foundation in Shaanxi Province[No.2018JM6029]the Key Research and Development Program of Shaanxi[No.2022GY-331].
文摘The WSN(wireless sensor network)node optimization problem faces the challenge of efficient deployment and adaptation under limited resources and a dynamically changing environment.The complex and changing deployment environment puts higher requirements on the search space,computational cost,and optimization efficiency of the algorithms.For this reason,a slime mould algorithm called SCA-SMA is proposed to solve the above problem.In SCA-SMA,a reverse Sobol sequence is used to initialize the population to increase the population diversity and improve the probability of approaching the optimal solution.To better balance local exploitation and global exploration,a dynamic selection of sine cosine update mechanism is proposed:using an optimal position selection mechanism in the global exploration phase to avoid local optima,and integrating the sine cosine algorithm in the local exploitation phase to improve the mucilage position update method,enrich the optimization search process and enhance the development capability of the algorithm.Finally,an adaptive mutation strategy can be proposed to increase the search range of the algorithm and motivate SCA-SMA to explore more promising regions.To evaluate the performance of the algorithm,SCA-SMA is experimentally validated in five different aspects.The results show that SCA-SMA is significantly competitive compared to advanced MAs.In particular,in facing the WSN node coverage problem,SCA-SMA has more obvious advantages in both average coverage and optimal coverage,which makes it possible to fully utilize the sensing range of each sensor node,while avoiding the waste of resources and the generation of monitoring blind zones.
文摘The Sine and Wormhole Energy Whale Optimization Algorithm(SWEWOA)represents an advanced solution method for resolving Optimal Power Flow(OPF)problems in power systems equipped with Flexible AC Transmission System(FACTS)devices which include Thyristor-Controlled Series Compensator(TCSC),Thyristor-Controlled Phase Shifter(TCPS),and Static Var Compensator(SVC).SWEWOA expands Whale Optimization Algorithm(WOA)through the integration of sine and wormhole energy features thus improving exploration and exploitation capabilities for efficient convergence in complex non-linear OPF problems.A performance evaluation of SWEWOA takes place on the IEEE-30 bus test system through static and dynamic loading scenarios where it demonstrates better results than five contemporary algorithms:Adaptive Chaotic WOA(ACWOA),WOA,Chaotic WOA(CWOA),Sine Cosine Algorithm Differential Evolution(SCADE),and Hybrid Grey Wolf Optimization(HGWO).The research shows that SWEWOA delivers superior generation cost reduction than other algorithms by reaching a minimum of 0.9%better performance.SWEWOA demonstrates superior power loss performance by achieving(P_(loss,min))at the lowest level compared to all other tested algorithms which leads to better system energy efficiency.The dynamic loading performance of SWEWOA leads to a 4.38%reduction in gross costs which proves its capability to handle different operating conditions.The algorithm achieves top performance in Friedman Rank Test(FRT)assessments through multiple performance metrics which verifies its consistent reliability and strong stability during changing power demands.The repeated simulations show that SWEWOA generates mean costs(C_(gen,min))and mean power loss values(P_(loss,min))with small deviations which indicate its capability to maintain cost-effective solutions in each simulation run.SWEWOA demonstrates great potential as an advanced optimization solution for power system operations through the results presented in this study.
基金supported in part by the National Natural Science Foundational of China under Grants 62373363 and 52007190
文摘Active damping(AD)strategy is an economical and efficient method to solve the resonant problem of the permanent magnet synchronous motor(PMSM)drive system with inductor-capacitor(LC)sine wave filter.In this article,the AD methods used in PMSM drive system are classified as inherent damping(ID),state variable feedback,and digital filter.Based on this,the purpose of this article is to provide an overview and analysis of the AD methods on PMSM drive system in recent years,and to comprehensively review,compare,and summarize the stability,dynamic performance,robustness,and algorithm complexity.Furthermore,a new expansion of AD method based on capacitor current feedback with high-pass filter(HPF-CCF)is studied to ensure the effectiveness when the resonant frequency is around sixth of the sampling frequency.The simulation and experimental results validate the effectiveness of theoretical analysis.
