This paper presents an Eulerian-Lagrangian algorithm for direct numerical simulation(DNS)of particle-laden flows.The algorithm is applicable to perform simulations of dilute suspensions of small inertial particles in ...This paper presents an Eulerian-Lagrangian algorithm for direct numerical simulation(DNS)of particle-laden flows.The algorithm is applicable to perform simulations of dilute suspensions of small inertial particles in turbulent carrier flow.The Eulerian framework numerically resolves turbulent carrier flow using a parallelized,finite-volume DNS solver on a staggered Cartesian grid.Particles are tracked using a point-particle method utilizing a Lagrangian particle tracking(LPT)algorithm.The proposed Eulerian-Lagrangian algorithm is validated using an inertial particle-laden turbulent channel flow for different Stokes number cases.The particle concentration profiles and higher-order statistics of the carrier and dispersed phases agree well with the benchmark results.We investigated the effect of fluid velocity interpolation and numerical integration schemes of particle tracking algorithms on particle dispersion statistics.The suitability of fluid velocity interpolation schemes for predicting the particle dispersion statistics is discussed in the framework of the particle tracking algorithm coupled to the finite-volume solver.In addition,we present parallelization strategies implemented in the algorithm and evaluate their parallel performance.展开更多
Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion...Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.展开更多
The single-point bending method,based on atomic force microscopy(AFM),has been extensively validated for characterizing the structural mechanical properties of micro-and nanobeams.Nevertheless,the influence of AFM pro...The single-point bending method,based on atomic force microscopy(AFM),has been extensively validated for characterizing the structural mechanical properties of micro-and nanobeams.Nevertheless,the influence of AFM probe loading and positioning has yet to be subjected to comprehensive investigation.This paper proposes a novel bending-test method based on sequential loading points,in which a series of evenly distributed loads are applied along the length of the central axis on the upper surface of the cantilever.The preliminary measured values of Young’s modulus for an unknown alloy material were 193,178,and 176 GPa,exhibiting a considerable degree of dispersion.An algorithm for self-correction of the positioning error was developed,and this resulted in a positioning error of 53 nm and a final converged Young’s modulus of 161 GPa.展开更多
Due to the inability of manufacturing a single monolithic mirror at the 10-meter scales,segmented mirrors have become indispensable tools in modern astronomical research.However,to match the imaging performance of the...Due to the inability of manufacturing a single monolithic mirror at the 10-meter scales,segmented mirrors have become indispensable tools in modern astronomical research.However,to match the imaging performance of the monolithic counterpart,the sub-mirrors must maintain precise co-phasing.Piston error critically degrades segmented mirror imaging quality,necessitating efficient and precise detection.To ad-dress the limitations that the conventional circular-aperture diffraction with two-wavelength algorithm is sus-ceptible to decentration errors,and the traditional convolutional neural networks(CNNs)struggle to capture global features under large-range piston errors due to their restricted local receptive fields,this paper pro-poses a method that integrates extended Young’s interference principles with a Vision Transformer(ViT)to detect piston error.By suppressing decentration error interference through two symmetrically arranged aper-tures and extending the measurement range to±7.95μm via a two-wavelength(589 nm/600 nm)algorithm.This approach exploits ViT’s self-attention mechanism to model global characteristics of interference fringes.Unlike CNNs constrained by local convolutional kernels,the ViT significantly improves sensitivity to inter-ferogram periodicity.The simulation results demonstrate that the proposed method achieves a measurement accuracy of 5 nm(0.0083λ0)across the range of±7.95μm,while maintaining an accuracy exceeding 95%in the presence of Gaussian noise(SNR≥15 dB),Poisson noise(λ≥9 photons/pixel),and sub-mirror gap er-ror(Egap≤0.2)interference.Moreover,the detection speed shows significant improvement compared to the cross-correlation algorithm.This study establishes an accurate,robust framework for segmented mirror error detection,advancing high-precision astronomical observation.展开更多
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
Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT^(*)),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narr...Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT^(*)),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narrow passages,slow convergence,and high computational costs.To address these challenges,this paper proposes a novel hybrid global path planning algorithm integrating Gaussian sampling and quadtree optimization(RRT^(*)-GSQ).This methodology aims to enhance path planning by synergistically combining a Gaussian mixture sampling strategy to improve node generation in critical regions,an adaptive step-size and direction optimization mechanism for enhanced obstacle avoidance,a Quadtree-AABB collision detection framework to lower computational complexity,and a dynamic iteration control strategy for more efficient convergence.In obstacle-free and obstructed scenarios,compared with the conventional RRT^(*),the proposed algorithm reduced the number of node evaluations by 67.57%and 62.72%,and decreased the search time by 79.72%and 78.52%,respectively.In path tracking tests,the proposed algorithm achieved substantial reductions in RMSE of the final path compared to the conventional RRT^(*).Specifically,the lateral RMSE was reduced by 41.5%in obstacle-free environments and 59.3%in obstructed environments,while the longitudinal RMSE was reduced by 57.2%and 58.5%,respectively.Furthermore,the maximum absolute errors in both lateral and longitudinal directions were constrained within 0.75 m.Field validation experiments in an operational orchard confirmed the algorithm's practical effectiveness,showing reductions in the mean tracking error of 47.6%(obstacle-free)and 58.3%(with obstructed),alongside a 5.1%and 7.2%shortening of the path length compared to the baseline method.The proposed algorithm effectively enhances path planning efficiency and navigation accuracy for robots,presenting a superior solution for high-precision autonomous navigation of agricultural robots in orchard environments and holding significant value for engineering applications.展开更多
We study the split common solution problem with multiple output sets for monotone operator equations in Hilbert spaces.To solve this problem,we propose two new parallel algorithms.We establish a weak convergence theor...We study the split common solution problem with multiple output sets for monotone operator equations in Hilbert spaces.To solve this problem,we propose two new parallel algorithms.We establish a weak convergence theorem for the first and a strong convergence theorem for the second.展开更多
With the transformation from websites to Internet platforms, Chinese young netizens (born in 1990-2005) have become key subjects in the evolution of cyber-nationalism. Based on survey data, this study classifies their...With the transformation from websites to Internet platforms, Chinese young netizens (born in 1990-2005) have become key subjects in the evolution of cyber-nationalism. Based on survey data, this study classifies their nationalism into four types and explores its transformation alongside globalization cognition. The result shows that moderate nationalism is the mainstream. This has raised their attention to globalization, with greater focus on relations between China and developing countries, and nations along the Belt and Road Initiative. Their personal experiences and cultural exposure foster a more inclusive global vision, shaping the evolution of nationalism and global dialogue.展开更多
Objective:Intergenerational ambivalence is the conflicts or differences in opinion occurring between 2 or more generations.Advanced technology,busy lifestyles,working parents,etc.,have widened the area of ambivalence ...Objective:Intergenerational ambivalence is the conflicts or differences in opinion occurring between 2 or more generations.Advanced technology,busy lifestyles,working parents,etc.,have widened the area of ambivalence between the mother and young adult.Therefore,the present study aimed to assess the intergenerational ambivalence and psychological well-being of mothers and young adults.Methods:A descriptive correlational survey was done among 150 participants(75 mothers and 75 young adults)of the Shirva panchayat area of Udupi taluk,Karnataka,India.Participants were selected based on the study inclusion criteria.Data were obtained using the demographic pro forma,followed by the Intergenerational Ambivalence Scale and Ryff Psychological Wellbeing Scale.Results:Based on the objectives and hypotheses of the study,the data analysis was done with SPSS version 20(IBM Corporation,Armonk,New York,United States).The mean age of mothers was 46.20 years,and that of young adults was 21.49 years.Both mothers and young adults have moderate levels of intergenerational ambivalence with scores of 70.7% and 73.3%,respectively.The psychological well-being of 57.3% of mothers was at a moderate level.A significant relationship was found between intergenerational ambivalence and psychological well-being(ρ=-0.035,P=0.0767).Conclusions:The present study concluded that as the intergenerational ambivalence of mothers increases,their psychological wellbeing decreases.展开更多
This paper introduces a novel nature-inspired metaheuristic algorithm called the Gekko japonicus algorithm.The algo-rithm draws inspiration mainly from the predation strategies and survival behaviors of the Gekko japo...This paper introduces a novel nature-inspired metaheuristic algorithm called the Gekko japonicus algorithm.The algo-rithm draws inspiration mainly from the predation strategies and survival behaviors of the Gekko japonicus.The math-ematical model is developed by simulating various biological behaviors of the Gekko japonicus,such as hybrid loco-motion patterns,directional olfactory guidance,implicit group advantage tendencies,and the tail autotomy mechanism.By integrating multi-stage mutual constraints and dynamically adjusting parameters,GJA maintains an optimal balance between global exploration and local exploitation,thereby effectively solving complex optimization problems.To assess the performance of GJA,comparative analyses were performed against fourteen state-of-the-art metaheuristic algorithms using the CEC2017 and CEC2022 benchmark test sets.Additionally,a Friedman test was performed on the experimen-tal results to assess the statistical significance of differences between various algorithms.And GJA was evaluated using multiple qualitative indicators,further confirming its superiority in exploration and exploitation.Finally,GJA was utilized to solve four engineering optimization problems and further implemented in robotic path planning to verify its practical applicability.Experimental results indicate that,compared to other high-performance algorithms,GJA demonstrates excep-tional performance as a powerful optimization algorithm in complex optimization problems.We make the code publicly available at:https://github.com/zhy1109/Gekko-japonicusalgorithm.展开更多
The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,convention...The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,conventional clustering-based methods face notable drawbacks,including poor scalability in handling high-dimensional datasets and a strong dependence of outcomes on initial conditions.To overcome the performance limitations of existing methods,this study proposes a novel quantum-inspired clustering algorithm that relies on a similarity coefficient-based quantum genetic algorithm(SC-QGA)and an improved quantum artificial bee colony algorithm hybrid K-means(IQABC-K).First,the SC-QGA algorithmis constructed based on quantum computing and integrates similarity coefficient theory to strengthen genetic diversity and feature extraction capabilities.For the subsequent clustering phase,the process based on the IQABC-K algorithm is enhanced with the core improvement of adaptive rotation gate and movement exploitation strategies to balance the exploration capabilities of global search and the exploitation capabilities of local search.Simultaneously,the acceleration of convergence toward the global optimum and a reduction in computational complexity are facilitated by means of the global optimum bootstrap strategy and a linear population reduction strategy.Through experimental evaluation with multiple algorithms and diverse performance metrics,the proposed algorithm confirms reliable accuracy on three datasets:KDD CUP99,NSL_KDD,and UNSW_NB15,achieving accuracy of 98.57%,98.81%,and 98.32%,respectively.These results affirm its potential as an effective solution for practical clustering applications.展开更多
Objective To determine the proportions of drug-resistant tuberculosis(TB),its trends,and the drug resistance-conferring mutations among patients with pulmonary TB aged 10-24 years in China.Methods The data of patients...Objective To determine the proportions of drug-resistant tuberculosis(TB),its trends,and the drug resistance-conferring mutations among patients with pulmonary TB aged 10-24 years in China.Methods The data of patients with pulmonary TB were retrieved from a national drug-resistant TB survey for analysis.Joinpoint regression software was used to analyze time trends.We also used whole genome sequencing to analyze the lineages and drug resistance-conferring mutations of 621 isolates.Results Among 4,235 patients with pulmonary TB,the proportion of new cases of multidrug-resistant tuberculosis(MDR-TB)was 3.18%(95%confidence interval[CI]:2.37-4.15)for adolescents and 3.76%(95%CI:3.03-4.60)for young adults;for previously treated patients,MDR-TB accounted for 11.25%(95%CI:5.28-20.28)of adolescents and 11.05%(95%CI:6.88-16.55)of young adults.The proportion of patients with MDR-TB remained stable among both new and previously treated patients aged 10-24 years during the study period.Through whole genome sequencing,we found that the most common mutations in the MDR-TB strains were Ser315Thr in the katG gene(71.74%)and Ser450Leu in the rpoB gene(50.00%).Conclusion This study revealed a high proportion of MDR-TB among adolescents and young adults,indicating that urgent and comprehensive measures are needed to reduce the emergence and transmission of drug-resistant TB among this population in China.展开更多
In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic h...In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services.展开更多
The uniquely Chinese educator spirit provides a fundamental guideline for teachers in the new era to fulfill their mission of moral education and talent cultivation.There exists a profound alignment between the teachi...The uniquely Chinese educator spirit provides a fundamental guideline for teachers in the new era to fulfill their mission of moral education and talent cultivation.There exists a profound alignment between the teaching innovation practices of young teachers and the core tenets of the educator spirit.Consequently,a three-dimensional framework for systematic guidance can be constructed focusing on values,wisdom,and resilience.Guided by ideals,beliefs,and moral integrity,the foundational values of teaching innovation are solidified,ensuring it remains anchored to the direction of nurturing students.Through educational wisdom and benevolence,the practical rationality of innovation is refined,propelling teaching toward greater scientific rigor and humanistic care.展开更多
Introduction,Breast cancer is the most common cancer type in adolescents and young adults<40 years of age,accounting for 30%of cancers in this age group1.Breast cancer in the young presents significant challenges f...Introduction,Breast cancer is the most common cancer type in adolescents and young adults<40 years of age,accounting for 30%of cancers in this age group1.Breast cancer in the young presents significant challenges for patients and society,including more aggressive tumor biology,poor prognosis,genetic susceptibility,fertility preservation,and complex psychosocial issues.Moreover,because of the markedly younger median age of breast cancer,the proportion of young breast cancer patients in China is significantly higher than Western countries2.The first Young Breast Cancer in China(YBCC)consensus meeting was held in Guangzhou,China in December 2021 to address exclusive challenges and requirements facing young patients with breast cancer.Chinese medical experts from multiple specialties had an extensive discussion and formulated a consensus over several hot topics in young patients with breast cancer.The“Expert Consensus on the Diagnosis and Treatment of Young Breast Cancer in China(2022 edition)”published in the Chinese Medical Journal has garnered significant attention3,highlighting enormous interest in the YBCC consensus in the medical community and public.展开更多
To address the issue of abnormal energy consumption fluctuations in the converter steelmaking process,an integrated diagnostic method combining the gray wolf optimization(GWO)algorithm,support vector machine(SVM),and ...To address the issue of abnormal energy consumption fluctuations in the converter steelmaking process,an integrated diagnostic method combining the gray wolf optimization(GWO)algorithm,support vector machine(SVM),and K-means clustering was proposed.Eight input parameters—derived from molten iron conditions and external factors—were selected as feature variables.A GWO-SVM model was developed to accurately predict the energy consumption of individual heats.Based on the prediction results,the mean absolute percentage error and maximum relative error of the test set were employed as criteria to identify heats with abnormal energy usage.For these heats,the K-means clustering algorithm was used to determine benchmark values of influencing factors from similar steel grades,enabling root-cause diagnosis of excessive energy consumption.The proposed method was applied to real production data from a converter in a steel plant.The analysis reveals that heat sample No.44 exhibits abnormal energy consumption,due to gas recovery being 1430.28 kg of standard coal below the benchmark level.A secondary contributing factor is a steam recovery shortfall of 237.99 kg of standard coal.This integrated approach offers a scientifically grounded tool for energy management in converter operations and provides valuable guidance for optimizing process parameters and enhancing energy efficiency.展开更多
ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the ...ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the algorithmhas attracted considerable academic and industrial interest owing to its effective balance between exploration and exploitation,coupled with advantages in real-time performance and robustness.Nevertheless,as applications have diversified,limitations in convergence precision and a tendency toward premature convergence have become increasingly evident,highlighting a need for improvement.This reviewsystematically outlines the developmental trajectory of the PIO algorithm,with a particular focus on its core applications in UAV navigation,multi-objective formulations,and a spectrum of variantmodels that have emerged in recent years.It offers a structured analysis of the foundational principles underlying the PIO.It conducts a comparative assessment of various performance-enhanced versions,including hybrid models that integrate mechanisms from other optimization paradigms.Additionally,the strengths andweaknesses of distinct PIOvariants are critically examined frommultiple perspectives,including intrinsic algorithmic characteristics,suitability for specific application scenarios,objective function design,and the rigor of the statistical evaluation methodologies employed in empirical studies.Finally,this paper identifies principal challenges within current PIO research and proposes several prospective research directions.Future work should focus on mitigating premature convergence by refining the two-phase search structure and adjusting the exponential decrease of individual numbers during the landmark operator.Enhancing parameter adaptation strategies,potentially using reinforcement learning for dynamic tuning,and advancing theoretical analyses on convergence and complexity are also critical.Further applications should be explored in constrained path planning,Neural Architecture Search(NAS),and other real-worldmulti-objective problems.For Multi-objective PIO(MPIO),key improvements include controlling the growth of the external archive and designing more effective selection mechanisms to maintain convergence efficiency.These efforts are expected to strengthen both the theoretical foundation and practical versatility of PIO and its variants.展开更多
In recent years,Blockchain Technology has become a paradigm shift,providing Transparent,Secure,and Decentralized platforms for diverse applications,ranging from Cryptocurrency to supply chain management.Nevertheless,t...In recent years,Blockchain Technology has become a paradigm shift,providing Transparent,Secure,and Decentralized platforms for diverse applications,ranging from Cryptocurrency to supply chain management.Nevertheless,the optimization of blockchain networks remains a critical challenge due to persistent issues such as latency,scalability,and energy consumption.This study proposes an innovative approach to Blockchain network optimization,drawing inspiration from principles of biological evolution and natural selection through evolutionary algorithms.Specifically,we explore the application of genetic algorithms,particle swarm optimization,and related evolutionary techniques to enhance the performance of blockchain networks.The proposed methodologies aim to optimize consensus mechanisms,improve transaction throughput,and reduce resource consumption.Through extensive simulations and real-world experiments,our findings demonstrate significant improvements in network efficiency,scalability,and stability.This research offers a thorough analysis of existing optimization techniques,introduces novel strategies,and assesses their efficacy based on empirical outputs.展开更多
With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-...With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-consuming and labor-intensive,but they also struggle to provide consistent,high-precision detection and realtime monitoring of pavement surface defects.To overcome these limitations,we propose an Automatic Recognition of PavementDefect(ARPD)algorithm,which leverages unmanned aerial vehicle(UAV)-based aerial imagery to automate the inspection process.The ARPD framework incorporates a backbone network based on the Selective State Space Model(S3M),which is designed to capture long-range temporal dependencies.This enables effective modeling of dynamic correlations among redundant and often repetitive structures commonly found in road imagery.Furthermore,a neck structure based on Semantics and Detail Infusion(SDI)is introduced to guide cross-scale feature fusion.The SDI module enhances the integration of low-level spatial details with high-level semantic cues,thereby improving feature expressiveness and defect localization accuracy.Experimental evaluations demonstrate that theARPDalgorithm achieves a mean average precision(mAP)of 86.1%on a custom-labeled pavement defect dataset,outperforming the state-of-the-art YOLOv11 segmentation model.The algorithm also maintains strong generalization ability on public datasets.These results confirm that ARPD is well-suited for diverse real-world applications in intelligent,large-scale highway defect monitoring and maintenance planning.展开更多
基金supported by the P.G.Senapathy Center for Computing Resources at IIT Madrasfunding provided by the Ministry of Education,Government of Indiasupported by the National Natural Science Foundation of China(Grant Nos.12388101,12472224 and 92252104).
文摘This paper presents an Eulerian-Lagrangian algorithm for direct numerical simulation(DNS)of particle-laden flows.The algorithm is applicable to perform simulations of dilute suspensions of small inertial particles in turbulent carrier flow.The Eulerian framework numerically resolves turbulent carrier flow using a parallelized,finite-volume DNS solver on a staggered Cartesian grid.Particles are tracked using a point-particle method utilizing a Lagrangian particle tracking(LPT)algorithm.The proposed Eulerian-Lagrangian algorithm is validated using an inertial particle-laden turbulent channel flow for different Stokes number cases.The particle concentration profiles and higher-order statistics of the carrier and dispersed phases agree well with the benchmark results.We investigated the effect of fluid velocity interpolation and numerical integration schemes of particle tracking algorithms on particle dispersion statistics.The suitability of fluid velocity interpolation schemes for predicting the particle dispersion statistics is discussed in the framework of the particle tracking algorithm coupled to the finite-volume solver.In addition,we present parallelization strategies implemented in the algorithm and evaluate their parallel performance.
文摘Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.
文摘The single-point bending method,based on atomic force microscopy(AFM),has been extensively validated for characterizing the structural mechanical properties of micro-and nanobeams.Nevertheless,the influence of AFM probe loading and positioning has yet to be subjected to comprehensive investigation.This paper proposes a novel bending-test method based on sequential loading points,in which a series of evenly distributed loads are applied along the length of the central axis on the upper surface of the cantilever.The preliminary measured values of Young’s modulus for an unknown alloy material were 193,178,and 176 GPa,exhibiting a considerable degree of dispersion.An algorithm for self-correction of the positioning error was developed,and this resulted in a positioning error of 53 nm and a final converged Young’s modulus of 161 GPa.
文摘Due to the inability of manufacturing a single monolithic mirror at the 10-meter scales,segmented mirrors have become indispensable tools in modern astronomical research.However,to match the imaging performance of the monolithic counterpart,the sub-mirrors must maintain precise co-phasing.Piston error critically degrades segmented mirror imaging quality,necessitating efficient and precise detection.To ad-dress the limitations that the conventional circular-aperture diffraction with two-wavelength algorithm is sus-ceptible to decentration errors,and the traditional convolutional neural networks(CNNs)struggle to capture global features under large-range piston errors due to their restricted local receptive fields,this paper pro-poses a method that integrates extended Young’s interference principles with a Vision Transformer(ViT)to detect piston error.By suppressing decentration error interference through two symmetrically arranged aper-tures and extending the measurement range to±7.95μm via a two-wavelength(589 nm/600 nm)algorithm.This approach exploits ViT’s self-attention mechanism to model global characteristics of interference fringes.Unlike CNNs constrained by local convolutional kernels,the ViT significantly improves sensitivity to inter-ferogram periodicity.The simulation results demonstrate that the proposed method achieves a measurement accuracy of 5 nm(0.0083λ0)across the range of±7.95μm,while maintaining an accuracy exceeding 95%in the presence of Gaussian noise(SNR≥15 dB),Poisson noise(λ≥9 photons/pixel),and sub-mirror gap er-ror(Egap≤0.2)interference.Moreover,the detection speed shows significant improvement compared to the cross-correlation algorithm.This study establishes an accurate,robust framework for segmented mirror error detection,advancing high-precision astronomical observation.
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 Natural Science Foundation of China(32301712)Natural Science Foundation of Jiangsu Province(BK20230548+3 种基金BK20250876)Project of Faculty of Agricultural Equipment of Jiangsu University(NGXB20240203)A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD-2023-87)Open Funding Project of the Key Laboratory of Modern Agricultural Equipment and Technology(Jiangsu University),Ministry of Education(MAET202101)。
文摘Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT^(*)),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narrow passages,slow convergence,and high computational costs.To address these challenges,this paper proposes a novel hybrid global path planning algorithm integrating Gaussian sampling and quadtree optimization(RRT^(*)-GSQ).This methodology aims to enhance path planning by synergistically combining a Gaussian mixture sampling strategy to improve node generation in critical regions,an adaptive step-size and direction optimization mechanism for enhanced obstacle avoidance,a Quadtree-AABB collision detection framework to lower computational complexity,and a dynamic iteration control strategy for more efficient convergence.In obstacle-free and obstructed scenarios,compared with the conventional RRT^(*),the proposed algorithm reduced the number of node evaluations by 67.57%and 62.72%,and decreased the search time by 79.72%and 78.52%,respectively.In path tracking tests,the proposed algorithm achieved substantial reductions in RMSE of the final path compared to the conventional RRT^(*).Specifically,the lateral RMSE was reduced by 41.5%in obstacle-free environments and 59.3%in obstructed environments,while the longitudinal RMSE was reduced by 57.2%and 58.5%,respectively.Furthermore,the maximum absolute errors in both lateral and longitudinal directions were constrained within 0.75 m.Field validation experiments in an operational orchard confirmed the algorithm's practical effectiveness,showing reductions in the mean tracking error of 47.6%(obstacle-free)and 58.3%(with obstructed),alongside a 5.1%and 7.2%shortening of the path length compared to the baseline method.The proposed algorithm effectively enhances path planning efficiency and navigation accuracy for robots,presenting a superior solution for high-precision autonomous navigation of agricultural robots in orchard environments and holding significant value for engineering applications.
基金supported by the Science and Technology Fund of TNU-Thai Nguyen University of Science.
文摘We study the split common solution problem with multiple output sets for monotone operator equations in Hilbert spaces.To solve this problem,we propose two new parallel algorithms.We establish a weak convergence theorem for the first and a strong convergence theorem for the second.
文摘With the transformation from websites to Internet platforms, Chinese young netizens (born in 1990-2005) have become key subjects in the evolution of cyber-nationalism. Based on survey data, this study classifies their nationalism into four types and explores its transformation alongside globalization cognition. The result shows that moderate nationalism is the mainstream. This has raised their attention to globalization, with greater focus on relations between China and developing countries, and nations along the Belt and Road Initiative. Their personal experiences and cultural exposure foster a more inclusive global vision, shaping the evolution of nationalism and global dialogue.
文摘Objective:Intergenerational ambivalence is the conflicts or differences in opinion occurring between 2 or more generations.Advanced technology,busy lifestyles,working parents,etc.,have widened the area of ambivalence between the mother and young adult.Therefore,the present study aimed to assess the intergenerational ambivalence and psychological well-being of mothers and young adults.Methods:A descriptive correlational survey was done among 150 participants(75 mothers and 75 young adults)of the Shirva panchayat area of Udupi taluk,Karnataka,India.Participants were selected based on the study inclusion criteria.Data were obtained using the demographic pro forma,followed by the Intergenerational Ambivalence Scale and Ryff Psychological Wellbeing Scale.Results:Based on the objectives and hypotheses of the study,the data analysis was done with SPSS version 20(IBM Corporation,Armonk,New York,United States).The mean age of mothers was 46.20 years,and that of young adults was 21.49 years.Both mothers and young adults have moderate levels of intergenerational ambivalence with scores of 70.7% and 73.3%,respectively.The psychological well-being of 57.3% of mothers was at a moderate level.A significant relationship was found between intergenerational ambivalence and psychological well-being(ρ=-0.035,P=0.0767).Conclusions:The present study concluded that as the intergenerational ambivalence of mothers increases,their psychological wellbeing decreases.
基金CHINA POSTDOCTORAL SCIENCE FOUNDATION(Grant No.2025M771925)Young Scientists Fund(C Class)(Grant No.32501636)Special Fund of Fundamental Scientific Research Business Expense for Higher School of Central Government(Grant No.2572025JT04).
文摘This paper introduces a novel nature-inspired metaheuristic algorithm called the Gekko japonicus algorithm.The algo-rithm draws inspiration mainly from the predation strategies and survival behaviors of the Gekko japonicus.The math-ematical model is developed by simulating various biological behaviors of the Gekko japonicus,such as hybrid loco-motion patterns,directional olfactory guidance,implicit group advantage tendencies,and the tail autotomy mechanism.By integrating multi-stage mutual constraints and dynamically adjusting parameters,GJA maintains an optimal balance between global exploration and local exploitation,thereby effectively solving complex optimization problems.To assess the performance of GJA,comparative analyses were performed against fourteen state-of-the-art metaheuristic algorithms using the CEC2017 and CEC2022 benchmark test sets.Additionally,a Friedman test was performed on the experimen-tal results to assess the statistical significance of differences between various algorithms.And GJA was evaluated using multiple qualitative indicators,further confirming its superiority in exploration and exploitation.Finally,GJA was utilized to solve four engineering optimization problems and further implemented in robotic path planning to verify its practical applicability.Experimental results indicate that,compared to other high-performance algorithms,GJA demonstrates excep-tional performance as a powerful optimization algorithm in complex optimization problems.We make the code publicly available at:https://github.com/zhy1109/Gekko-japonicusalgorithm.
基金supported by the NSFC(Grant Nos.62176273,62271070,62441212)The Open Foundation of State Key Laboratory of Networking and Switching Technology(Beijing University of Posts and Telecommunications)under Grant SKLNST-2024-1-062025Major Project of the Natural Science Foundation of Inner Mongolia(2025ZD008).
文摘The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,conventional clustering-based methods face notable drawbacks,including poor scalability in handling high-dimensional datasets and a strong dependence of outcomes on initial conditions.To overcome the performance limitations of existing methods,this study proposes a novel quantum-inspired clustering algorithm that relies on a similarity coefficient-based quantum genetic algorithm(SC-QGA)and an improved quantum artificial bee colony algorithm hybrid K-means(IQABC-K).First,the SC-QGA algorithmis constructed based on quantum computing and integrates similarity coefficient theory to strengthen genetic diversity and feature extraction capabilities.For the subsequent clustering phase,the process based on the IQABC-K algorithm is enhanced with the core improvement of adaptive rotation gate and movement exploitation strategies to balance the exploration capabilities of global search and the exploitation capabilities of local search.Simultaneously,the acceleration of convergence toward the global optimum and a reduction in computational complexity are facilitated by means of the global optimum bootstrap strategy and a linear population reduction strategy.Through experimental evaluation with multiple algorithms and diverse performance metrics,the proposed algorithm confirms reliable accuracy on three datasets:KDD CUP99,NSL_KDD,and UNSW_NB15,achieving accuracy of 98.57%,98.81%,and 98.32%,respectively.These results affirm its potential as an effective solution for practical clustering applications.
基金funded by the National Key Research and Development Program of China(grant number 2022YFC2305204).
文摘Objective To determine the proportions of drug-resistant tuberculosis(TB),its trends,and the drug resistance-conferring mutations among patients with pulmonary TB aged 10-24 years in China.Methods The data of patients with pulmonary TB were retrieved from a national drug-resistant TB survey for analysis.Joinpoint regression software was used to analyze time trends.We also used whole genome sequencing to analyze the lineages and drug resistance-conferring mutations of 621 isolates.Results Among 4,235 patients with pulmonary TB,the proportion of new cases of multidrug-resistant tuberculosis(MDR-TB)was 3.18%(95%confidence interval[CI]:2.37-4.15)for adolescents and 3.76%(95%CI:3.03-4.60)for young adults;for previously treated patients,MDR-TB accounted for 11.25%(95%CI:5.28-20.28)of adolescents and 11.05%(95%CI:6.88-16.55)of young adults.The proportion of patients with MDR-TB remained stable among both new and previously treated patients aged 10-24 years during the study period.Through whole genome sequencing,we found that the most common mutations in the MDR-TB strains were Ser315Thr in the katG gene(71.74%)and Ser450Leu in the rpoB gene(50.00%).Conclusion This study revealed a high proportion of MDR-TB among adolescents and young adults,indicating that urgent and comprehensive measures are needed to reduce the emergence and transmission of drug-resistant TB among this population in China.
基金funding from the European Commission by the Ruralities project(grant agreement no.101060876).
文摘In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services.
文摘The uniquely Chinese educator spirit provides a fundamental guideline for teachers in the new era to fulfill their mission of moral education and talent cultivation.There exists a profound alignment between the teaching innovation practices of young teachers and the core tenets of the educator spirit.Consequently,a three-dimensional framework for systematic guidance can be constructed focusing on values,wisdom,and resilience.Guided by ideals,beliefs,and moral integrity,the foundational values of teaching innovation are solidified,ensuring it remains anchored to the direction of nurturing students.Through educational wisdom and benevolence,the practical rationality of innovation is refined,propelling teaching toward greater scientific rigor and humanistic care.
基金supported by the National Natural Science Foundation of China(Grant Nos.82230057 and 82272859)the National Key R&D Program of China(Grant No.2022YFC2505101).
文摘Introduction,Breast cancer is the most common cancer type in adolescents and young adults<40 years of age,accounting for 30%of cancers in this age group1.Breast cancer in the young presents significant challenges for patients and society,including more aggressive tumor biology,poor prognosis,genetic susceptibility,fertility preservation,and complex psychosocial issues.Moreover,because of the markedly younger median age of breast cancer,the proportion of young breast cancer patients in China is significantly higher than Western countries2.The first Young Breast Cancer in China(YBCC)consensus meeting was held in Guangzhou,China in December 2021 to address exclusive challenges and requirements facing young patients with breast cancer.Chinese medical experts from multiple specialties had an extensive discussion and formulated a consensus over several hot topics in young patients with breast cancer.The“Expert Consensus on the Diagnosis and Treatment of Young Breast Cancer in China(2022 edition)”published in the Chinese Medical Journal has garnered significant attention3,highlighting enormous interest in the YBCC consensus in the medical community and public.
基金support from the National Key R&D Program of China(Grant No.2020YFB1711100).
文摘To address the issue of abnormal energy consumption fluctuations in the converter steelmaking process,an integrated diagnostic method combining the gray wolf optimization(GWO)algorithm,support vector machine(SVM),and K-means clustering was proposed.Eight input parameters—derived from molten iron conditions and external factors—were selected as feature variables.A GWO-SVM model was developed to accurately predict the energy consumption of individual heats.Based on the prediction results,the mean absolute percentage error and maximum relative error of the test set were employed as criteria to identify heats with abnormal energy usage.For these heats,the K-means clustering algorithm was used to determine benchmark values of influencing factors from similar steel grades,enabling root-cause diagnosis of excessive energy consumption.The proposed method was applied to real production data from a converter in a steel plant.The analysis reveals that heat sample No.44 exhibits abnormal energy consumption,due to gas recovery being 1430.28 kg of standard coal below the benchmark level.A secondary contributing factor is a steam recovery shortfall of 237.99 kg of standard coal.This integrated approach offers a scientifically grounded tool for energy management in converter operations and provides valuable guidance for optimizing process parameters and enhancing energy efficiency.
基金supported by the National Natural Science Foundation of China under grant number 62066016the Natural Science Foundation of Hunan Province of China under grant number 2024JJ7395+2 种基金International and Regional Science and Technology Cooperation and Exchange Program of the Hunan Association for Science and Technology under grant number 025SKX-KJ-04Hunan Provincial Postgraduate Research Innovation Project under grant numberCX20251611Liye Qin Bamboo Slips Research Special Project of JishouUniversity 25LYY03.
文摘ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the algorithmhas attracted considerable academic and industrial interest owing to its effective balance between exploration and exploitation,coupled with advantages in real-time performance and robustness.Nevertheless,as applications have diversified,limitations in convergence precision and a tendency toward premature convergence have become increasingly evident,highlighting a need for improvement.This reviewsystematically outlines the developmental trajectory of the PIO algorithm,with a particular focus on its core applications in UAV navigation,multi-objective formulations,and a spectrum of variantmodels that have emerged in recent years.It offers a structured analysis of the foundational principles underlying the PIO.It conducts a comparative assessment of various performance-enhanced versions,including hybrid models that integrate mechanisms from other optimization paradigms.Additionally,the strengths andweaknesses of distinct PIOvariants are critically examined frommultiple perspectives,including intrinsic algorithmic characteristics,suitability for specific application scenarios,objective function design,and the rigor of the statistical evaluation methodologies employed in empirical studies.Finally,this paper identifies principal challenges within current PIO research and proposes several prospective research directions.Future work should focus on mitigating premature convergence by refining the two-phase search structure and adjusting the exponential decrease of individual numbers during the landmark operator.Enhancing parameter adaptation strategies,potentially using reinforcement learning for dynamic tuning,and advancing theoretical analyses on convergence and complexity are also critical.Further applications should be explored in constrained path planning,Neural Architecture Search(NAS),and other real-worldmulti-objective problems.For Multi-objective PIO(MPIO),key improvements include controlling the growth of the external archive and designing more effective selection mechanisms to maintain convergence efficiency.These efforts are expected to strengthen both the theoretical foundation and practical versatility of PIO and its variants.
文摘In recent years,Blockchain Technology has become a paradigm shift,providing Transparent,Secure,and Decentralized platforms for diverse applications,ranging from Cryptocurrency to supply chain management.Nevertheless,the optimization of blockchain networks remains a critical challenge due to persistent issues such as latency,scalability,and energy consumption.This study proposes an innovative approach to Blockchain network optimization,drawing inspiration from principles of biological evolution and natural selection through evolutionary algorithms.Specifically,we explore the application of genetic algorithms,particle swarm optimization,and related evolutionary techniques to enhance the performance of blockchain networks.The proposed methodologies aim to optimize consensus mechanisms,improve transaction throughput,and reduce resource consumption.Through extensive simulations and real-world experiments,our findings demonstrate significant improvements in network efficiency,scalability,and stability.This research offers a thorough analysis of existing optimization techniques,introduces novel strategies,and assesses their efficacy based on empirical outputs.
基金supported in part by the Technical Service for the Development and Application of an Intelligent Visual Management Platformfor Expressway Construction Progress Based on BIM Technology(grant NO.JKYZLX-2023-09)in partby the Technical Service for the Development of an Early Warning Model in the Research and Application of Key Technologies for Tunnel Operation Safety Monitoring and Early Warning Based on Digital Twin(grant NO.JK-S02-ZNGS-202412-JISHU-FA-0035)sponsored by Yunnan Transportation Science Research Institute Co.,Ltd.
文摘With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-consuming and labor-intensive,but they also struggle to provide consistent,high-precision detection and realtime monitoring of pavement surface defects.To overcome these limitations,we propose an Automatic Recognition of PavementDefect(ARPD)algorithm,which leverages unmanned aerial vehicle(UAV)-based aerial imagery to automate the inspection process.The ARPD framework incorporates a backbone network based on the Selective State Space Model(S3M),which is designed to capture long-range temporal dependencies.This enables effective modeling of dynamic correlations among redundant and often repetitive structures commonly found in road imagery.Furthermore,a neck structure based on Semantics and Detail Infusion(SDI)is introduced to guide cross-scale feature fusion.The SDI module enhances the integration of low-level spatial details with high-level semantic cues,thereby improving feature expressiveness and defect localization accuracy.Experimental evaluations demonstrate that theARPDalgorithm achieves a mean average precision(mAP)of 86.1%on a custom-labeled pavement defect dataset,outperforming the state-of-the-art YOLOv11 segmentation model.The algorithm also maintains strong generalization ability on public datasets.These results confirm that ARPD is well-suited for diverse real-world applications in intelligent,large-scale highway defect monitoring and maintenance planning.
