Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises stru...Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises structural weight under stress and frequency constraints.Two new algorithms,the Red Kite Optimization Algorithm(ROA)and Secretary Bird Optimization Algorithm(SBOA),are utilized on five benchmark trusses with 10,18,37,72,and 200-bar trusses.Both algorithms are evaluated against benchmarks in the literature.The results indicate that SBOA always reaches a lighter optimal.Designs with reducing structural weight ranging from 0.02%to 0.15%compared to ROA,and up to 6%–8%as compared to conventional algorithms.In addition,SBOA can achieve 15%–20%faster convergence speed and 10%–18%reduction in computational time with a smaller standard deviation over independent runs,which demonstrates its robustness and reliability.It is indicated that the adaptive exploration mechanism of SBOA,especially its Levy flight–based search strategy,can obviously improve optimization performance for low-and high-dimensional trusses.The research has implications in the context of promoting bio-inspired optimization techniques by demonstrating the viability of SBOA,a reliable model for large-scale structural design that provides significant enhancements in performance and convergence behavior.展开更多
Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting...Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach.展开更多
Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered so...Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered soils remains a complex challenge.This study presents a novel application of five ensemble machine(ML)algorithms-random forest(RF),gradient boosting machine(GBM),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),and categorical boosting(CatBoost)-to predict the undrained bearing capacity factor(Nc)of circular open caissons embedded in two-layered clay on the basis of results from finite element limit analysis(FELA).The input dataset consists of 1188 numerical simulations using the Tresca failure criterion,varying in geometrical and soil parameters.The FELA was performed via OptumG2 software with adaptive meshing techniques and verified against existing benchmark studies.The ML models were trained on 70% of the dataset and tested on the remaining 30%.Their performance was evaluated using six statistical metrics:coefficient of determination(R²),mean absolute error(MAE),root mean squared error(RMSE),index of scatter(IOS),RMSE-to-standard deviation ratio(RSR),and variance explained factor(VAF).The results indicate that all the models achieved high accuracy,with R²values exceeding 97.6%and RMSE values below 0.02.Among them,AdaBoost and CatBoost consistently outperformed the other methods across both the training and testing datasets,demonstrating superior generalizability and robustness.The proposed ML framework offers an efficient,accurate,and data-driven alternative to traditional methods for estimating caisson capacity in stratified soils.This approach can aid in reducing computational costs while improving reliability in the early stages of foundation design.展开更多
The word“spatial”fundamentally relates to human existence,evolution,and activity in terrestrial and even celestial spaces.After reviewing the spatial features of many areas,the paper describes basics of high level m...The word“spatial”fundamentally relates to human existence,evolution,and activity in terrestrial and even celestial spaces.After reviewing the spatial features of many areas,the paper describes basics of high level model and technology called Spatial Grasp for dealing with large distributed systems,which can provide spatial vision,awareness,management,control,and even consciousness.The technology description includes its key Spatial Grasp Language(SGL),self-evolution of recursive SGL scenarios,and implementation of SGL interpreter converting distributed networked systems into powerful spatial engines.Examples of typical spatial scenarios in SGL include finding shortest path tree and shortest path between network nodes,collecting proper information throughout the whole world,elimination of multiple targets by intelligent teams of chasers,and withstanding cyber attacks in distributed networked systems.Also this paper compares Spatial Grasp model with traditional algorithms,confirming universality of the former for any spatial systems,while the latter just tools for concrete applications.展开更多
The Cross-domain Heuristic Search Challenge(CHeSC)is a competition focused on creating efficient search algorithms adaptable to diverse problem domains.Selection hyper-heuristics are a class of algorithms that dynamic...The Cross-domain Heuristic Search Challenge(CHeSC)is a competition focused on creating efficient search algorithms adaptable to diverse problem domains.Selection hyper-heuristics are a class of algorithms that dynamically choose heuristics during the search process.Numerous selection hyper-heuristics have different imple-mentation strategies.However,comparisons between them are lacking in the literature,and previous works have not highlighted the beneficial and detrimental implementation methods of different components.The question is how to effectively employ them to produce an efficient search heuristic.Furthermore,the algorithms that competed in the inaugural CHeSC have not been collectively reviewed.This work conducts a review analysis of the top twenty competitors from this competition to identify effective and ineffective strategies influencing algorithmic performance.A summary of the main characteristics and classification of the algorithms is presented.The analysis underlines efficient and inefficient methods in eight key components,including search points,search phases,heuristic selection,move acceptance,feedback,Tabu mechanism,restart mechanism,and low-level heuristic parameter control.This review analyzes the components referencing the competition’s final leaderboard and discusses future research directions for these components.The effective approaches,identified as having the highest quality index,are mixed search point,iterated search phases,relay hybridization selection,threshold acceptance,mixed learning,Tabu heuristics,stochastic restart,and dynamic parameters.Findings are also compared with recent trends in hyper-heuristics.This work enhances the understanding of selection hyper-heuristics,offering valuable insights for researchers and practitioners aiming to develop effective search algorithms for diverse problem domains.展开更多
Heuristic optimization algorithms have been widely used in solving complex optimization problems in various fields such as engineering,economics,and computer science.These algorithms are designed to find high-quality ...Heuristic optimization algorithms have been widely used in solving complex optimization problems in various fields such as engineering,economics,and computer science.These algorithms are designed to find high-quality solutions efficiently by balancing exploration of the search space and exploitation of promising solutions.While heuristic optimization algorithms vary in their specific details,they often exhibit common patterns that are essential to their effectiveness.This paper aims to analyze and explore common patterns in heuristic optimization algorithms.Through a comprehensive review of the literature,we identify the patterns that are commonly observed in these algorithms,including initialization,local search,diversity maintenance,adaptation,and stochasticity.For each pattern,we describe the motivation behind it,its implementation,and its impact on the search process.To demonstrate the utility of our analysis,we identify these patterns in multiple heuristic optimization algorithms.For each case study,we analyze how the patterns are implemented in the algorithm and how they contribute to its performance.Through these case studies,we show how our analysis can be used to understand the behavior of heuristic optimization algorithms and guide the design of new algorithms.Our analysis reveals that patterns in heuristic optimization algorithms are essential to their effectiveness.By understanding and incorporating these patterns into the design of new algorithms,researchers can develop more efficient and effective optimization algorithms.展开更多
Risk prediction has long been a cornerstone of surgical oncology,enabling surgeons to anticipate complications,tailor perioperative care,and improve outcomes.With the rise of artificial intelligence,machine learning(M...Risk prediction has long been a cornerstone of surgical oncology,enabling surgeons to anticipate complications,tailor perioperative care,and improve outcomes.With the rise of artificial intelligence,machine learning(ML)models are increasingly being applied to predict outcomes,highlighting the growing significance of data-driven methods for clinical decision-making.Currently,frequentist approaches dominate prediction models,including most ML algorithms;these rely exclusively on observed datasets and risk overlooking the cumulative value of prior clinical knowledge.In contrast,Bayesian reasoning formally integrates existing evidence with new data.In this letter,we examine the strengths of frequentist-based prediction models,discuss how Bayesian methods may improve predictive accuracy,and argue that combining both approaches offers a promising path toward more robust,interpretable,and clinically useful prediction tools in surgery.This integration can yield robust,interpretable,and clinically relevant tools that advance personalized surgical care.展开更多
The rapid advancement of 6G communication technologies and generative artificial intelligence(AI)is catalyzing a new wave of innovation at the intersection of networking and intelligent computing.On the one hand,6G en...The rapid advancement of 6G communication technologies and generative artificial intelligence(AI)is catalyzing a new wave of innovation at the intersection of networking and intelligent computing.On the one hand,6G envisions a hyper-connected environment that supports ubiquitous intelligence through ultra-low latency,high throughput,massive device connectivity,and integrated sensing and communication.On the other hand,generative AI,powered by large foundation models,has emerged as a powerful paradigm capable of creating.展开更多
Neuromorphic computing extends beyond sequential processing modalities and outperforms traditional von Neumann architectures in implementing more complicated tasks,e.g.,pattern processing,image recognition,and decisio...Neuromorphic computing extends beyond sequential processing modalities and outperforms traditional von Neumann architectures in implementing more complicated tasks,e.g.,pattern processing,image recognition,and decision making.It features parallel interconnected neural networks,high fault tolerance,robustness,autonomous learning capability,and ultralow energy dissipation.The algorithms of artificial neural network(ANN)have also been widely used because of their facile self-organization and self-learning capabilities,which mimic those of the human brain.To some extent,ANN reflects several basic functions of the human brain and can be efficiently integrated into neuromorphic devices to perform neuromorphic computations.This review highlights recent advances in neuromorphic devices assisted by machine learning algorithms.First,the basic structure of simple neuron models inspired by biological neurons and the information processing in simple neural networks are particularly discussed.Second,the fabrication and research progress of neuromorphic devices are presented regarding to materials and structures.Furthermore,the fabrication of neuromorphic devices,including stand-alone neuromorphic devices,neuromorphic device arrays,and integrated neuromorphic systems,is discussed and demonstrated with reference to some respective studies.The applications of neuromorphic devices assisted by machine learning algorithms in different fields are categorized and investigated.Finally,perspectives,suggestions,and potential solutions to the current challenges of neuromorphic devices are provided.展开更多
Multi-Objective Evolutionary Algorithms(MOEAs)have significantly advanced the domain of MultiObjective Optimization(MOO),facilitating solutions for complex problems with multiple conflicting objectives.This review exp...Multi-Objective Evolutionary Algorithms(MOEAs)have significantly advanced the domain of MultiObjective Optimization(MOO),facilitating solutions for complex problems with multiple conflicting objectives.This review explores the historical development of MOEAs,beginning with foundational concepts in multi-objective optimization,basic types of MOEAs,and the evolution of Pareto-based selection and niching methods.Further advancements,including decom-position-based approaches and hybrid algorithms,are discussed.Applications are analyzed in established domains such as engineering and economics,as well as in emerging fields like advanced analytics and machine learning.The significance of MOEAs in addressing real-world problems is emphasized,highlighting their role in facilitating informed decision-making.Finally,the development trajectory of MOEAs is compared with evolutionary processes,offering insights into their progress and future potential.展开更多
Objective:To identify potential key genes associated with pre-eclampsia through bioinformatics analysis,construct predictive models using machine-learning algorithms,and evaluate the models'performance in predicti...Objective:To identify potential key genes associated with pre-eclampsia through bioinformatics analysis,construct predictive models using machine-learning algorithms,and evaluate the models'performance in predicting pre-eclampsia.Methods:Gene-expression microarray datasets GSE10588,GSE66273,and GSE30186 related to pre-eclampsia were downloaded from the gene expression omnibus(GEO).Data were normalized using R,and differentially expressed genes(DEGs)were identified.LASSO regression was applied to further filter DEGs.Based on the selected DEGs,six machine-learning models-logistic regression(LR),random forest(RF),support vector machine(SVM),K-nearest neighbors(KNN),neural network(NN),and eXtreme gradient boosting(XGBoost)were built in R,and their performance was validated.Results:From the three datasets,a total of 1,363 genes were extracted.LASSO regression narrowed these to 265 candidate key genes.Multivariate analysis ultimately identified four genes closely associated with pre-eclampsia:EVI5,GCLM,LEP,and SYNPO2L.Using these four key genes,six machine-learning models were constructed.Receiver operating characteristic(ROC)analysis showed that all models achieved AUC>0.9:LR(AUC=0.983,95%CI=0.942-0.998),RF(AUC=0.961,95%CI=0.912-0.987),SVM(AUC=0.936,95%CI=0.879-0.972),KNN(AUC=0.970,95%CI=0.924-0.992),NN(AUC=0.916,95%CI=0.854-0.958),and XGBoost(AUC=0.952,95%CI=0.900-0.982).There was no statistically significant difference among the AUCs of the models(P>0.05).Conclusion:This study identified four key genes linked to preeclampsia through integrated bioinformatics analysis.Predictive models built on these genes can accurately forecast the occurrence of pre-eclampsia,suggesting that the four genes may serve as potential biomarkers for early diagnosis and therapeutic targeting of pre-eclampsia.展开更多
Swarm intelligence,derived from the collective behaviour of biological entities,is a novel methodology for overseeing satellite constellations within decentralized control systems.Conventional centralized control syst...Swarm intelligence,derived from the collective behaviour of biological entities,is a novel methodology for overseeing satellite constellations within decentralized control systems.Conventional centralized control systems in satellite constellations encounter constraints in scalability,resilience,and fault tolerance,particularly in extensive constellations.This research examines the use of swarm-based multi-agent systems and distributed algorithms for efficient communication,collision avoidance,and collaborative task execution in satellite constellations.We provide a comprehensive study of current swarm control algorithms,their relevance to satellite systems,and identify areas requiring further research.Principal subjects encompass decentralized decision-making,self-organization,adaptive communication protocols,and collision-free trajectory planning.The review article examines implementation obstacles,contrasts swarm algorithms with traditional approaches,and delineates potential research avenues to improve the autonomy and efficiency of satellite constellations.The detailed Literature and comparative analysis within this manuscript demonstrate the prospective advantages of swarm intelligence in satellite systems,providing insights for academic researchers and industry personnel alike.展开更多
Wind energy has emerged as a potential replacement for fossil fuel-based energy sources.To harness maximum wind energy,a crucial decision in the development of an efficient wind farm is the optimal layout design.This ...Wind energy has emerged as a potential replacement for fossil fuel-based energy sources.To harness maximum wind energy,a crucial decision in the development of an efficient wind farm is the optimal layout design.This layout defines the specific locations of the turbines within the wind farm.The process of finding the optimal locations of turbines,in the presence of various technical and technological constraints,makes the wind farm layout design problem a complex optimization problem.This problem has traditionally been solved with nature-inspired algorithms with promising results.The performance and convergence of nature-inspired algorithms depend on several parameters,among which the algorithm termination criterion plays a crucial role.Timely convergence is an important aspect of efficient algorithm design because an inefficient algorithm results in wasted computational resources,unwarranted electricity consumption,and hardware stress.This study provides an in-depth analysis of several termination criteria while using the genetic algorithm as a test bench,with its application to the wind farm layout design problem while considering various wind scenarios.The performance of six termination criteria is empirically evaluated with respect to the quality of solutions produced and the execution time involved.Due to the conflicting nature of these two attributes,fuzzy logic-based multi-attribute decision-making is employed in the decision process.Results for the fuzzy decision approach indicate that among the various criteria tested,the criterion Phi achieves an improvement in the range of 2.44%to 32.93%for wind scenario 1.For scenario 2,Best-worst termination criterion performed well compared to the other criteria evaluated,with an improvement in the range of 1.2%to 9.64%.For scenario 3,Hitting bound was the best performer with an improvement of 1.16%to 20.93%.展开更多
This study addresses the critical challenge of reconfiguration in unbalanced power distribution networks(UPDNs),focusing on the complex 123-Bus test system.Three scenarios are investigated:(1)simultaneous power loss r...This study addresses the critical challenge of reconfiguration in unbalanced power distribution networks(UPDNs),focusing on the complex 123-Bus test system.Three scenarios are investigated:(1)simultaneous power loss reduction and voltage profile improvement,(2)minimization of voltage and current unbalance indices under various operational cases,and(3)multi-objective optimization using Pareto front analysis to concurrently optimize voltage unbalance index,active power loss,and current unbalance index.Unlike previous research that oftensimplified system components,this work maintains all equipment,including capacitor banks,transformers,and voltage regulators,to ensure realistic results.The study evaluates twelve metaheuristic algorithms to solve the reconfiguration problem(RecPrb)in UPDNs.A comprehensive statistical analysis is conducted to identify the most efficient algorithm for solving the RecPrb in the 123-Bus UPDN,employing multiple performance metrics and comparative techniques.The Artificial Hummingbird Algorithm emerges as the top-performing algorithm and is subsequently applied to address a multi-objective optimization challenge in the 123-Bus UPDN.This research contributes valuable insights for network operators and researchers in selecting suitable algorithms for specific reconfiguration scenarios,advancing the field of UPDN optimization and management.展开更多
The headquarters of Plutus Financial Group Ltd,based in Hong Kong of China,stands as a silent yet razorsharp marker—rooted deeply in the heart of traditional finance,while piercing boundaries,exploring the vast nebul...The headquarters of Plutus Financial Group Ltd,based in Hong Kong of China,stands as a silent yet razorsharp marker—rooted deeply in the heart of traditional finance,while piercing boundaries,exploring the vast nebulae of blockchain and artificial intelligence.This integrated financial services group,newly listed on Nasdaq this February,is moving through the cut-and-thrust of the capital market with the postureof a"white knight."展开更多
Natural Language Processing(NLP)has become essential in text classification,sentiment analysis,machine translation,and speech recognition applications.As these tasks become complex,traditionalmachine learning and deep...Natural Language Processing(NLP)has become essential in text classification,sentiment analysis,machine translation,and speech recognition applications.As these tasks become complex,traditionalmachine learning and deep learning models encounter challenges with optimization,parameter tuning,and handling large-scale,highdimensional data.Bio-inspired algorithms,which mimic natural processes,offer robust optimization capabilities that can enhance NLP performance by improving feature selection,optimizing model parameters,and integrating adaptive learning mechanisms.This review explores the state-of-the-art applications of bio-inspired algorithms—such as Genetic Algorithms(GA),Particle Swarm Optimization(PSO),and Ant Colony Optimization(ACO)—across core NLP tasks.We analyze their comparative advantages,discuss their integration with neural network models,and address computational and scalability limitations.Through a synthesis of existing research,this paper highlights the unique strengths and current challenges of bio-inspired approaches in NLP,offering insights into hybrid models and lightweight,resource-efficient adaptations for real-time processing.Finally,we outline future research directions that emphasize the development of scalable,effective bio-inspired methods adaptable to evolving data environments.展开更多
Background:In recent years,there has been a growing trend in the utilization of observational studies that make use of routinely collected healthcare data(RCD).These studies rely on algorithms to identify specific hea...Background:In recent years,there has been a growing trend in the utilization of observational studies that make use of routinely collected healthcare data(RCD).These studies rely on algorithms to identify specific health conditions(e.g.,diabetes or sepsis)for statistical analyses.However,there has been substantial variation in the algorithm development and validation,leading to frequently suboptimal performance and posing a significant threat to the validity of study findings.Unfortunately,these issues are often overlooked.Methods:We systematically developed guidance for the development,validation,and evaluation of algorithms designed to identify health status(DEVELOP-RCD).Our initial efforts involved conducting both a narrative review and a systematic review of published studies on the concepts and methodological issues related to algorithm development,validation,and evaluation.Subsequently,we conducted an empirical study on an algorithm for identifying sepsis.Based on these findings,we formulated specific workflow and recommendations for algorithm development,validation,and evaluation within the guidance.Finally,the guidance underwent independent review by a panel of 20 external experts who then convened a consensus meeting to finalize it.Results:A standardized workflow for algorithm development,validation,and evaluation was established.Guided by specific health status considerations,the workflow comprises four integrated steps:assessing an existing algorithm’s suitability for the target health status;developing a new algorithm using recommended methods;validating the algorithm using prescribed performance measures;and evaluating the impact of the algorithm on study results.Additionally,13 good practice recommendations were formulated with detailed explanations.Furthermore,a practical study on sepsis identification was included to demonstrate the application of this guidance.Conclusions:The establishment of guidance is intended to aid researchers and clinicians in the appropriate and accurate development and application of algorithms for identifying health status from RCD.This guidance has the potential to enhance the credibility of findings from observational studies involving RCD.展开更多
The distribution of shear-wave velocities in the subsurface is generally used to assess the potential forseismic liquefaction and soil amplification effects and to classify seismic sites. Newly developeddistributed ac...The distribution of shear-wave velocities in the subsurface is generally used to assess the potential forseismic liquefaction and soil amplification effects and to classify seismic sites. Newly developeddistributed acoustic sensing (DAS) technology enables estimation of the shear-wave distribution as ahigh-density seismic observation system. This technology is characterized by low maintenance costs,high-resolution outputs, and real-time data transmission capabilities, albeit with the challenge ofmanaging massive data generation. Rapid and efficient interpretation of data is the key to advancingapplication of the DAS technology. In this study, field tests were carried out to record ambient noise overa short period using DAS technology, from which the surface-wave dispersion curves were extracted. Inorder to reduce the influence of directional effects on the results, an unsupervised clustering method isused to select appropriate clusters to extract the Green's function. A combination of a genetic algorithmand Monte Carlo (GA-MC) simulation is proposed to invert the subsurface velocity structure. Thestratigraphic profiles obtained by the GA-MC method are in agreement with the borehole profiles.Compared to other methods, the proposed optimization method not only improves the solution qualitybut also reduces the solution time.展开更多
In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classificati...In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classification methods that utilize evolutionary algorithms(EAs)for gene expression profiles in cancer or medical applications based on research motivations,challenges,and recommendations.Relevant studies were retrieved from four major academic databases-IEEE,Scopus,Springer,and ScienceDirect-using the keywords‘cancer classification’,‘optimization’,‘FS’,and‘gene expression profile’.A total of 67 papers were finally selected with key advancements identified as follows:(1)The majority of papers(44.8%)focused on developing algorithms and models for FS and classification.(2)The second category encompassed studies on biomarker identification by EAs,including 20 papers(30%).(3)The third category comprised works that applied FS to cancer data for decision support system purposes,addressing high-dimensional data and the formulation of chromosome length.These studies accounted for 12%of the total number of studies.(4)The remaining three papers(4.5%)were reviews and surveys focusing on models and developments in prediction and classification optimization for cancer classification under current technical conditions.This review highlights the importance of optimizing FS in EAs to manage high-dimensional data effectively.Despite recent advancements,significant limitations remain:the dynamic formulation of chromosome length remains an underexplored area.Thus,further research is needed on dynamic-length chromosome techniques for more sophisticated biomarker gene selection techniques.The findings suggest that further advancements in dynamic chromosome length formulations and adaptive algorithms could enhance cancer classification accuracy and efficiency.展开更多
Quantum computing offers unprecedented computational power, enabling simultaneous computations beyond traditional computers. Quantum computers differ significantly from classical computers, necessitating a distinct ap...Quantum computing offers unprecedented computational power, enabling simultaneous computations beyond traditional computers. Quantum computers differ significantly from classical computers, necessitating a distinct approach to algorithm design, which involves taming quantum mechanical phenomena. This paper extends the numbering of computable programs to be applied in the quantum computing context. Numbering computable programs is a theoretical computer science concept that assigns unique numbers to individual programs or algorithms. Common methods include Gödel numbering which encodes programs as strings of symbols or characters, often used in formal systems and mathematical logic. Based on the proposed numbering approach, this paper presents a mechanism to explore the set of possible quantum algorithms. The proposed approach is able to construct useful circuits such as Quantum Key Distribution BB84 protocol, which enables sender and receiver to establish a secure cryptographic key via a quantum channel. The proposed approach facilitates the process of exploring and constructing quantum algorithms.展开更多
文摘Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises structural weight under stress and frequency constraints.Two new algorithms,the Red Kite Optimization Algorithm(ROA)and Secretary Bird Optimization Algorithm(SBOA),are utilized on five benchmark trusses with 10,18,37,72,and 200-bar trusses.Both algorithms are evaluated against benchmarks in the literature.The results indicate that SBOA always reaches a lighter optimal.Designs with reducing structural weight ranging from 0.02%to 0.15%compared to ROA,and up to 6%–8%as compared to conventional algorithms.In addition,SBOA can achieve 15%–20%faster convergence speed and 10%–18%reduction in computational time with a smaller standard deviation over independent runs,which demonstrates its robustness and reliability.It is indicated that the adaptive exploration mechanism of SBOA,especially its Levy flight–based search strategy,can obviously improve optimization performance for low-and high-dimensional trusses.The research has implications in the context of promoting bio-inspired optimization techniques by demonstrating the viability of SBOA,a reliable model for large-scale structural design that provides significant enhancements in performance and convergence behavior.
基金National Key Research and Development Program of China,No.2023YFC3006704National Natural Science Foundation of China,No.42171047CAS-CSIRO Partnership Joint Project of 2024,No.177GJHZ2023097MI。
文摘Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach.
文摘Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered soils remains a complex challenge.This study presents a novel application of five ensemble machine(ML)algorithms-random forest(RF),gradient boosting machine(GBM),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),and categorical boosting(CatBoost)-to predict the undrained bearing capacity factor(Nc)of circular open caissons embedded in two-layered clay on the basis of results from finite element limit analysis(FELA).The input dataset consists of 1188 numerical simulations using the Tresca failure criterion,varying in geometrical and soil parameters.The FELA was performed via OptumG2 software with adaptive meshing techniques and verified against existing benchmark studies.The ML models were trained on 70% of the dataset and tested on the remaining 30%.Their performance was evaluated using six statistical metrics:coefficient of determination(R²),mean absolute error(MAE),root mean squared error(RMSE),index of scatter(IOS),RMSE-to-standard deviation ratio(RSR),and variance explained factor(VAF).The results indicate that all the models achieved high accuracy,with R²values exceeding 97.6%and RMSE values below 0.02.Among them,AdaBoost and CatBoost consistently outperformed the other methods across both the training and testing datasets,demonstrating superior generalizability and robustness.The proposed ML framework offers an efficient,accurate,and data-driven alternative to traditional methods for estimating caisson capacity in stratified soils.This approach can aid in reducing computational costs while improving reliability in the early stages of foundation design.
文摘The word“spatial”fundamentally relates to human existence,evolution,and activity in terrestrial and even celestial spaces.After reviewing the spatial features of many areas,the paper describes basics of high level model and technology called Spatial Grasp for dealing with large distributed systems,which can provide spatial vision,awareness,management,control,and even consciousness.The technology description includes its key Spatial Grasp Language(SGL),self-evolution of recursive SGL scenarios,and implementation of SGL interpreter converting distributed networked systems into powerful spatial engines.Examples of typical spatial scenarios in SGL include finding shortest path tree and shortest path between network nodes,collecting proper information throughout the whole world,elimination of multiple targets by intelligent teams of chasers,and withstanding cyber attacks in distributed networked systems.Also this paper compares Spatial Grasp model with traditional algorithms,confirming universality of the former for any spatial systems,while the latter just tools for concrete applications.
基金funded by Ministry of Higher Education(MoHE)Malaysia,under Transdisciplinary Research Grant Scheme(TRGS/1/2019/UKM/01/4/2).
文摘The Cross-domain Heuristic Search Challenge(CHeSC)is a competition focused on creating efficient search algorithms adaptable to diverse problem domains.Selection hyper-heuristics are a class of algorithms that dynamically choose heuristics during the search process.Numerous selection hyper-heuristics have different imple-mentation strategies.However,comparisons between them are lacking in the literature,and previous works have not highlighted the beneficial and detrimental implementation methods of different components.The question is how to effectively employ them to produce an efficient search heuristic.Furthermore,the algorithms that competed in the inaugural CHeSC have not been collectively reviewed.This work conducts a review analysis of the top twenty competitors from this competition to identify effective and ineffective strategies influencing algorithmic performance.A summary of the main characteristics and classification of the algorithms is presented.The analysis underlines efficient and inefficient methods in eight key components,including search points,search phases,heuristic selection,move acceptance,feedback,Tabu mechanism,restart mechanism,and low-level heuristic parameter control.This review analyzes the components referencing the competition’s final leaderboard and discusses future research directions for these components.The effective approaches,identified as having the highest quality index,are mixed search point,iterated search phases,relay hybridization selection,threshold acceptance,mixed learning,Tabu heuristics,stochastic restart,and dynamic parameters.Findings are also compared with recent trends in hyper-heuristics.This work enhances the understanding of selection hyper-heuristics,offering valuable insights for researchers and practitioners aiming to develop effective search algorithms for diverse problem domains.
文摘Heuristic optimization algorithms have been widely used in solving complex optimization problems in various fields such as engineering,economics,and computer science.These algorithms are designed to find high-quality solutions efficiently by balancing exploration of the search space and exploitation of promising solutions.While heuristic optimization algorithms vary in their specific details,they often exhibit common patterns that are essential to their effectiveness.This paper aims to analyze and explore common patterns in heuristic optimization algorithms.Through a comprehensive review of the literature,we identify the patterns that are commonly observed in these algorithms,including initialization,local search,diversity maintenance,adaptation,and stochasticity.For each pattern,we describe the motivation behind it,its implementation,and its impact on the search process.To demonstrate the utility of our analysis,we identify these patterns in multiple heuristic optimization algorithms.For each case study,we analyze how the patterns are implemented in the algorithm and how they contribute to its performance.Through these case studies,we show how our analysis can be used to understand the behavior of heuristic optimization algorithms and guide the design of new algorithms.Our analysis reveals that patterns in heuristic optimization algorithms are essential to their effectiveness.By understanding and incorporating these patterns into the design of new algorithms,researchers can develop more efficient and effective optimization algorithms.
文摘Risk prediction has long been a cornerstone of surgical oncology,enabling surgeons to anticipate complications,tailor perioperative care,and improve outcomes.With the rise of artificial intelligence,machine learning(ML)models are increasingly being applied to predict outcomes,highlighting the growing significance of data-driven methods for clinical decision-making.Currently,frequentist approaches dominate prediction models,including most ML algorithms;these rely exclusively on observed datasets and risk overlooking the cumulative value of prior clinical knowledge.In contrast,Bayesian reasoning formally integrates existing evidence with new data.In this letter,we examine the strengths of frequentist-based prediction models,discuss how Bayesian methods may improve predictive accuracy,and argue that combining both approaches offers a promising path toward more robust,interpretable,and clinically useful prediction tools in surgery.This integration can yield robust,interpretable,and clinically relevant tools that advance personalized surgical care.
文摘The rapid advancement of 6G communication technologies and generative artificial intelligence(AI)is catalyzing a new wave of innovation at the intersection of networking and intelligent computing.On the one hand,6G envisions a hyper-connected environment that supports ubiquitous intelligence through ultra-low latency,high throughput,massive device connectivity,and integrated sensing and communication.On the other hand,generative AI,powered by large foundation models,has emerged as a powerful paradigm capable of creating.
基金financially supported by the National Natural Science Foundation of China(No.52073031)the National Key Research and Development Program of China(Nos.2023YFB3208102,2021YFB3200304)+4 种基金the China National Postdoctoral Program for Innovative Talents(No.BX2021302)the Beijing Nova Program(Nos.Z191100001119047,Z211100002121148)the Fundamental Research Funds for the Central Universities(No.E0EG6801X2)the‘Hundred Talents Program’of the Chinese Academy of Sciencesthe BrainLink program funded by the MSIT through the NRF of Korea(No.RS-2023-00237308).
文摘Neuromorphic computing extends beyond sequential processing modalities and outperforms traditional von Neumann architectures in implementing more complicated tasks,e.g.,pattern processing,image recognition,and decision making.It features parallel interconnected neural networks,high fault tolerance,robustness,autonomous learning capability,and ultralow energy dissipation.The algorithms of artificial neural network(ANN)have also been widely used because of their facile self-organization and self-learning capabilities,which mimic those of the human brain.To some extent,ANN reflects several basic functions of the human brain and can be efficiently integrated into neuromorphic devices to perform neuromorphic computations.This review highlights recent advances in neuromorphic devices assisted by machine learning algorithms.First,the basic structure of simple neuron models inspired by biological neurons and the information processing in simple neural networks are particularly discussed.Second,the fabrication and research progress of neuromorphic devices are presented regarding to materials and structures.Furthermore,the fabrication of neuromorphic devices,including stand-alone neuromorphic devices,neuromorphic device arrays,and integrated neuromorphic systems,is discussed and demonstrated with reference to some respective studies.The applications of neuromorphic devices assisted by machine learning algorithms in different fields are categorized and investigated.Finally,perspectives,suggestions,and potential solutions to the current challenges of neuromorphic devices are provided.
文摘Multi-Objective Evolutionary Algorithms(MOEAs)have significantly advanced the domain of MultiObjective Optimization(MOO),facilitating solutions for complex problems with multiple conflicting objectives.This review explores the historical development of MOEAs,beginning with foundational concepts in multi-objective optimization,basic types of MOEAs,and the evolution of Pareto-based selection and niching methods.Further advancements,including decom-position-based approaches and hybrid algorithms,are discussed.Applications are analyzed in established domains such as engineering and economics,as well as in emerging fields like advanced analytics and machine learning.The significance of MOEAs in addressing real-world problems is emphasized,highlighting their role in facilitating informed decision-making.Finally,the development trajectory of MOEAs is compared with evolutionary processes,offering insights into their progress and future potential.
文摘Objective:To identify potential key genes associated with pre-eclampsia through bioinformatics analysis,construct predictive models using machine-learning algorithms,and evaluate the models'performance in predicting pre-eclampsia.Methods:Gene-expression microarray datasets GSE10588,GSE66273,and GSE30186 related to pre-eclampsia were downloaded from the gene expression omnibus(GEO).Data were normalized using R,and differentially expressed genes(DEGs)were identified.LASSO regression was applied to further filter DEGs.Based on the selected DEGs,six machine-learning models-logistic regression(LR),random forest(RF),support vector machine(SVM),K-nearest neighbors(KNN),neural network(NN),and eXtreme gradient boosting(XGBoost)were built in R,and their performance was validated.Results:From the three datasets,a total of 1,363 genes were extracted.LASSO regression narrowed these to 265 candidate key genes.Multivariate analysis ultimately identified four genes closely associated with pre-eclampsia:EVI5,GCLM,LEP,and SYNPO2L.Using these four key genes,six machine-learning models were constructed.Receiver operating characteristic(ROC)analysis showed that all models achieved AUC>0.9:LR(AUC=0.983,95%CI=0.942-0.998),RF(AUC=0.961,95%CI=0.912-0.987),SVM(AUC=0.936,95%CI=0.879-0.972),KNN(AUC=0.970,95%CI=0.924-0.992),NN(AUC=0.916,95%CI=0.854-0.958),and XGBoost(AUC=0.952,95%CI=0.900-0.982).There was no statistically significant difference among the AUCs of the models(P>0.05).Conclusion:This study identified four key genes linked to preeclampsia through integrated bioinformatics analysis.Predictive models built on these genes can accurately forecast the occurrence of pre-eclampsia,suggesting that the four genes may serve as potential biomarkers for early diagnosis and therapeutic targeting of pre-eclampsia.
文摘Swarm intelligence,derived from the collective behaviour of biological entities,is a novel methodology for overseeing satellite constellations within decentralized control systems.Conventional centralized control systems in satellite constellations encounter constraints in scalability,resilience,and fault tolerance,particularly in extensive constellations.This research examines the use of swarm-based multi-agent systems and distributed algorithms for efficient communication,collision avoidance,and collaborative task execution in satellite constellations.We provide a comprehensive study of current swarm control algorithms,their relevance to satellite systems,and identify areas requiring further research.Principal subjects encompass decentralized decision-making,self-organization,adaptive communication protocols,and collision-free trajectory planning.The review article examines implementation obstacles,contrasts swarm algorithms with traditional approaches,and delineates potential research avenues to improve the autonomy and efficiency of satellite constellations.The detailed Literature and comparative analysis within this manuscript demonstrate the prospective advantages of swarm intelligence in satellite systems,providing insights for academic researchers and industry personnel alike.
基金funded by King Fahd University of Petroleum&Minerals,Saudi Arabia under IRC-SES grant#INRE 2217.
文摘Wind energy has emerged as a potential replacement for fossil fuel-based energy sources.To harness maximum wind energy,a crucial decision in the development of an efficient wind farm is the optimal layout design.This layout defines the specific locations of the turbines within the wind farm.The process of finding the optimal locations of turbines,in the presence of various technical and technological constraints,makes the wind farm layout design problem a complex optimization problem.This problem has traditionally been solved with nature-inspired algorithms with promising results.The performance and convergence of nature-inspired algorithms depend on several parameters,among which the algorithm termination criterion plays a crucial role.Timely convergence is an important aspect of efficient algorithm design because an inefficient algorithm results in wasted computational resources,unwarranted electricity consumption,and hardware stress.This study provides an in-depth analysis of several termination criteria while using the genetic algorithm as a test bench,with its application to the wind farm layout design problem while considering various wind scenarios.The performance of six termination criteria is empirically evaluated with respect to the quality of solutions produced and the execution time involved.Due to the conflicting nature of these two attributes,fuzzy logic-based multi-attribute decision-making is employed in the decision process.Results for the fuzzy decision approach indicate that among the various criteria tested,the criterion Phi achieves an improvement in the range of 2.44%to 32.93%for wind scenario 1.For scenario 2,Best-worst termination criterion performed well compared to the other criteria evaluated,with an improvement in the range of 1.2%to 9.64%.For scenario 3,Hitting bound was the best performer with an improvement of 1.16%to 20.93%.
基金supported by the Scientific and Technological Research Council of Turkey(TUBITAK)under Grant No.124E002(1001-Project).
文摘This study addresses the critical challenge of reconfiguration in unbalanced power distribution networks(UPDNs),focusing on the complex 123-Bus test system.Three scenarios are investigated:(1)simultaneous power loss reduction and voltage profile improvement,(2)minimization of voltage and current unbalance indices under various operational cases,and(3)multi-objective optimization using Pareto front analysis to concurrently optimize voltage unbalance index,active power loss,and current unbalance index.Unlike previous research that oftensimplified system components,this work maintains all equipment,including capacitor banks,transformers,and voltage regulators,to ensure realistic results.The study evaluates twelve metaheuristic algorithms to solve the reconfiguration problem(RecPrb)in UPDNs.A comprehensive statistical analysis is conducted to identify the most efficient algorithm for solving the RecPrb in the 123-Bus UPDN,employing multiple performance metrics and comparative techniques.The Artificial Hummingbird Algorithm emerges as the top-performing algorithm and is subsequently applied to address a multi-objective optimization challenge in the 123-Bus UPDN.This research contributes valuable insights for network operators and researchers in selecting suitable algorithms for specific reconfiguration scenarios,advancing the field of UPDN optimization and management.
文摘The headquarters of Plutus Financial Group Ltd,based in Hong Kong of China,stands as a silent yet razorsharp marker—rooted deeply in the heart of traditional finance,while piercing boundaries,exploring the vast nebulae of blockchain and artificial intelligence.This integrated financial services group,newly listed on Nasdaq this February,is moving through the cut-and-thrust of the capital market with the postureof a"white knight."
基金supported by AIT Laboratory,FPT University,Danang Campus,Vietnam,2024.
文摘Natural Language Processing(NLP)has become essential in text classification,sentiment analysis,machine translation,and speech recognition applications.As these tasks become complex,traditionalmachine learning and deep learning models encounter challenges with optimization,parameter tuning,and handling large-scale,highdimensional data.Bio-inspired algorithms,which mimic natural processes,offer robust optimization capabilities that can enhance NLP performance by improving feature selection,optimizing model parameters,and integrating adaptive learning mechanisms.This review explores the state-of-the-art applications of bio-inspired algorithms—such as Genetic Algorithms(GA),Particle Swarm Optimization(PSO),and Ant Colony Optimization(ACO)—across core NLP tasks.We analyze their comparative advantages,discuss their integration with neural network models,and address computational and scalability limitations.Through a synthesis of existing research,this paper highlights the unique strengths and current challenges of bio-inspired approaches in NLP,offering insights into hybrid models and lightweight,resource-efficient adaptations for real-time processing.Finally,we outline future research directions that emphasize the development of scalable,effective bio-inspired methods adaptable to evolving data environments.
基金supported by the National Natural Science Foundation of China(82225049,72104155)the Sichuan Provincial Central Government Guides Local Science and Technology Development Special Project(2022ZYD0127)the 1·3·5 Project for Disciplines of Excellence,West China Hospital,Sichuan University(ZYGD23004).
文摘Background:In recent years,there has been a growing trend in the utilization of observational studies that make use of routinely collected healthcare data(RCD).These studies rely on algorithms to identify specific health conditions(e.g.,diabetes or sepsis)for statistical analyses.However,there has been substantial variation in the algorithm development and validation,leading to frequently suboptimal performance and posing a significant threat to the validity of study findings.Unfortunately,these issues are often overlooked.Methods:We systematically developed guidance for the development,validation,and evaluation of algorithms designed to identify health status(DEVELOP-RCD).Our initial efforts involved conducting both a narrative review and a systematic review of published studies on the concepts and methodological issues related to algorithm development,validation,and evaluation.Subsequently,we conducted an empirical study on an algorithm for identifying sepsis.Based on these findings,we formulated specific workflow and recommendations for algorithm development,validation,and evaluation within the guidance.Finally,the guidance underwent independent review by a panel of 20 external experts who then convened a consensus meeting to finalize it.Results:A standardized workflow for algorithm development,validation,and evaluation was established.Guided by specific health status considerations,the workflow comprises four integrated steps:assessing an existing algorithm’s suitability for the target health status;developing a new algorithm using recommended methods;validating the algorithm using prescribed performance measures;and evaluating the impact of the algorithm on study results.Additionally,13 good practice recommendations were formulated with detailed explanations.Furthermore,a practical study on sepsis identification was included to demonstrate the application of this guidance.Conclusions:The establishment of guidance is intended to aid researchers and clinicians in the appropriate and accurate development and application of algorithms for identifying health status from RCD.This guidance has the potential to enhance the credibility of findings from observational studies involving RCD.
基金supported by the National Natural Science Foundation of China(Grant Nos.42225702 and 42077235)the Natural Science Foundation of Jiangsu Province(Grant No.BK20211086)the open fund of the Key Laboratory of Earth Fissures Geological Disaster,Ministry of Natural Resources.
文摘The distribution of shear-wave velocities in the subsurface is generally used to assess the potential forseismic liquefaction and soil amplification effects and to classify seismic sites. Newly developeddistributed acoustic sensing (DAS) technology enables estimation of the shear-wave distribution as ahigh-density seismic observation system. This technology is characterized by low maintenance costs,high-resolution outputs, and real-time data transmission capabilities, albeit with the challenge ofmanaging massive data generation. Rapid and efficient interpretation of data is the key to advancingapplication of the DAS technology. In this study, field tests were carried out to record ambient noise overa short period using DAS technology, from which the surface-wave dispersion curves were extracted. Inorder to reduce the influence of directional effects on the results, an unsupervised clustering method isused to select appropriate clusters to extract the Green's function. A combination of a genetic algorithmand Monte Carlo (GA-MC) simulation is proposed to invert the subsurface velocity structure. Thestratigraphic profiles obtained by the GA-MC method are in agreement with the borehole profiles.Compared to other methods, the proposed optimization method not only improves the solution qualitybut also reduces the solution time.
基金funded by the Ministry of Higher Education of Malaysia,grant number FRGS/1/2022/ICT02/UPSI/02/1.
文摘In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classification methods that utilize evolutionary algorithms(EAs)for gene expression profiles in cancer or medical applications based on research motivations,challenges,and recommendations.Relevant studies were retrieved from four major academic databases-IEEE,Scopus,Springer,and ScienceDirect-using the keywords‘cancer classification’,‘optimization’,‘FS’,and‘gene expression profile’.A total of 67 papers were finally selected with key advancements identified as follows:(1)The majority of papers(44.8%)focused on developing algorithms and models for FS and classification.(2)The second category encompassed studies on biomarker identification by EAs,including 20 papers(30%).(3)The third category comprised works that applied FS to cancer data for decision support system purposes,addressing high-dimensional data and the formulation of chromosome length.These studies accounted for 12%of the total number of studies.(4)The remaining three papers(4.5%)were reviews and surveys focusing on models and developments in prediction and classification optimization for cancer classification under current technical conditions.This review highlights the importance of optimizing FS in EAs to manage high-dimensional data effectively.Despite recent advancements,significant limitations remain:the dynamic formulation of chromosome length remains an underexplored area.Thus,further research is needed on dynamic-length chromosome techniques for more sophisticated biomarker gene selection techniques.The findings suggest that further advancements in dynamic chromosome length formulations and adaptive algorithms could enhance cancer classification accuracy and efficiency.
文摘Quantum computing offers unprecedented computational power, enabling simultaneous computations beyond traditional computers. Quantum computers differ significantly from classical computers, necessitating a distinct approach to algorithm design, which involves taming quantum mechanical phenomena. This paper extends the numbering of computable programs to be applied in the quantum computing context. Numbering computable programs is a theoretical computer science concept that assigns unique numbers to individual programs or algorithms. Common methods include Gödel numbering which encodes programs as strings of symbols or characters, often used in formal systems and mathematical logic. Based on the proposed numbering approach, this paper presents a mechanism to explore the set of possible quantum algorithms. The proposed approach is able to construct useful circuits such as Quantum Key Distribution BB84 protocol, which enables sender and receiver to establish a secure cryptographic key via a quantum channel. The proposed approach facilitates the process of exploring and constructing quantum algorithms.
