基于Youngs界面重构技术的自适应网格细分方法

Euler型多物质流体动力学数值方法中,常采用Youngs界面重构技术处理混合网格。Youngs方法在输运步中需要同时考虑两种物质,程序编写复杂,效率低。本文在Youngs方法的基础上,对混合网格进行多层细分,用细分后的纯物质子网格代替原混合网格。分析了网格细分的具体方案;对细分后子网格,同时考虑往周边八个网格的输运,给出了所有可能分配量的表达式。最后编程实验结果表明,在不增加计算量的基础上,网格细分的方法有效地提高了计算精度。

Youngs方法在自膨胀浆液移动界面追踪中的应用

数值模拟是研究浆液在岩体裂隙中扩散规律的重要途径之一。对浆液运动界面进行准确地追踪,是建立浆液流动扩散仿真分析方法需要解决的一个关键问题。以自膨胀浆液在平板裂隙中流动扩散过程为对象,考虑浆液扩散过程中两相流特征,引入VOF函数描述两相介质分布,采用基于几何学原理的Youngs方法求解VOF方程,以实现对浆液移动界面的追踪。利用已知密度随时间变化规律的自膨胀浆液在平板裂隙中自由扩散算例对该方法进行测试,结果表明,不同时刻求解得到的浆液移动界面位置和轮廓与解析解吻合较好,显示了较高的计算精度和良好的界面追踪效果,为开发自膨胀浆液流动扩散仿真程序奠定了基础。

轴对称柱坐标系中的Youngs算法

为提高包含有交界面的轴对称流动问题的求解精度 ,将直角系中的Youngs算法公式推广到轴对称柱系中应用 .根据流体体积分数的定义 ,在直角系Youngs算法的基础上 ,通过引入修正系数 ,可以方便地实现Youngs算法在轴对称柱坐标系中的应用 .最后在轴对称情况下 ,数值模拟了空泡在理想流场中的溃灭过程 ,通过比较空泡半径理论解和由Youngs算法得到的数值解 ,检验Youngs算法在轴对称情况下的应用 .

PSO algorithm for Young's modulus reconstruction

To get the quantitive value of abnormal biological tissues, an inverse algorithm about the Young＇s modulus based on the boundary extraction and the image registration technologies is proposed. With the known displacements of boundary tissues and the force distribution, the Young＇s modulus is calculated by constructing the unit system and the inverse finite element method （IFEM）. Then a tough range of the modulus for the whole tissue is estimated referring the value obtained before. The improved particle swarm optimizer （PSO） method is adopted to calculate the whole Yong＇s modulus distribution. The presented algorithm overcomes some limitations in other Young＇s modulus reconstruction methods and relaxes the displacements and force boundary condition requirements. The repetitious numerical simulation shows that errors in boundary displacement are not very sensitive to the estimation of next process; a final feasible solution is obtained by the improved PSO method which is close to the theoretical values obtained during searching in an extensive range.

Youngs-VOF方法模拟溃坝水流演进

针对追踪溃坝洪水自由面变化一直为计算流体力学的难点问题,采用Youngs-VOF方法追踪流体流场自由面、交错网格离散求解区域、人工压缩方法求解粘性不可压缩流体控制方程,并通过数值计算模拟了溃坝洪水瞬变现象。结果表明,Youngs-VOF方法追踪运动交界面与MARTIN的试验结果吻合,且略优于Hirt-VOF法,为溃坝洪水的模拟提供了新途径。

基于Youngs界面技术的混凝土中爆炸三维动力计算

为了研究混凝土中爆炸的物理机制,基于三维Youngs界面重构技术,编制了MMIC-3 D程序,模拟了半无限混凝土中爆炸空腔的形成、发展、鼓包运动规律等过程,得到了半无限混凝土中爆炸的三维数值模拟结果.研究结果表明,计算结果与实验现象和物理规律一致,本文中的计算方法和程序代码是正确的,可以为工程设计提供依据.

一种基于几何重构的Youngs-VOF耦合水平集追踪方法

针对界面追踪方法中拉格朗日方法和欧拉–拉格朗日方法计算效率低、不适用大变形、不能应用于三维数值计算模型等问题,研究了一种效率高、界面清晰、适用于三维模型的计算气液两相界面迁移特性的欧拉运动界面追踪方法,该方法将'米'状相邻单元Youngs方法用于运动界面重构,将Youngs-VOF和水平集通过几何方法耦合,提高运动界面精度,克服了VOF和水平集方法存在的缺陷,避免了利用高阶导数本身的稳定性去求解水平集对流方程和距离函数方程.'米'状相邻单元Youngs方法避免了数值耗散、数值色散性以及非线性效应引起的捕捉界面模糊的情况. Youngs-VOF耦合水平集方法既保证了计算界面时的稳定性,与拉格朗日方法相比又提高了计算效率.利用Youngs-VOF耦合水平集方法与VOF方法对单个气泡在水中上升过程数值计算与实验对比并对经典剪切流场中圆形运动界面模型的数值计算,验证了Youngs-VOF耦合水平集方法的有效性并比VOF方法捕捉界面更清晰、锐利;通过对溃坝–自由表面流动过程数值计算并与实验进行对比,验证了Youngs-VOF耦合水平集方法的稳定性以及对三维数值模型的适用性.

A novel genomic prediction method combining randomized Haseman-Elston regression with a modified algorithm for Proven and Young for large genomic data

Computational efficiency has become a key issue in genomic prediction(GP) owing to the massive historical datasets accumulated. We developed hereby a new super-fast GP approach(SHEAPY) combining randomized Haseman-Elston regression(RHE-reg) with a modified Algorithm for Proven and Young(APY) in an additive-effect model, using the former to estimate heritability and then the latter to invert a large genomic relationship matrix for best linear prediction. In simulation results with varied sizes of training population, GBLUP, HEAPY|A and SHEAPY showed similar predictive performance when the size of a core population was half that of a large training population and the heritability was a fixed value, and the computational speed of SHEAPY was faster than that of GBLUP and HEAPY|A. In simulation results with varied heritability, SHEAPY showed better predictive ability than GBLUP in all cases and than HEAPY|A in most cases when the size of a core population was 4/5 that of a small training population and the training population size was a fixed value. As a proof of concept, SHEAPY was applied to the analysis of two real datasets. In an Arabidopsis thaliana F2 population, the predictive performance of SHEAPY was similar to or better than that of GBLUP and HEAPY|A in most cases when the size of a core population(2 0 0) was 2/3 of that of a small training population(3 0 0). In a sorghum multiparental population,SHEAPY showed higher predictive accuracy than HEAPY|A for all of three traits, and than GBLUP for two traits. SHEAPY may become the GP method of choice for large-scale genomic data.

Global,regional and national burdens of bipolar disorders in adolescents and young adults:a trend analysis from 1990 to 2019

Background Bipolar disorder is identified as a cause of severe damage to the physical,psychological and social functioning of adolescents and young adults.Aims The aim of this study is to ascertain the trends in the burden of bipolar disorder among individuals aged 10-24 years at global,regional and national levels from 1990 to 2019.Methods The data analysed in this study were from the Global Burden of Diseases 2019.The numbers,rates per 100000 population,average annual percentage changes(AAPCs)of incidence,prevalence and years lived with disability(YLDs)of bipolar disorder are reported at the global,regional and national levels among individuals aged 10-24 years.Global trends by age,sex and Social Development Index(SDI)were further analysed.Results Globally,the incidence of bipolar disorder among adolescents and young adults increased from 79.21 per 100000 population(95%uncertainty interval(Ul):58.13 to 105.15)in 1990 to 84.97 per 100000 population(95%Ul:61.73 to 113.46)in 2019,AAPC 0.24(95%confidence interval(Cl):0.22 to 0.26).In the past three decades,there has been an increase in incidence,prevalence and YLDs in both males and females.The largest increase in incidence between 1990 and 2019 was observed in those aged 20-24 years old from 51.76 per 100000 population(95%Ul:26.81 to 87.20)in 1990 to 58.37 per 100000 population(95%UI:30.39 to 98.55)in 2019;AAPC 0.42(95%Cl:0.38 to 0.47).By the SDI quintile,the largest increase in incidence was observed in the middle SDl;however,the high SDI countries had the highest incidence.Regionally,the largest increase in incidence was observed in southern Latin America.At the national level,the most pronounced increase in the incidence was in Greenland.Conclusions The global increase in incidence among adolescents and young adults between 1990 and 2019 indicates that strategies to improve their mental health still need to be emphasised.

Underwater four-quadrant dual-beam circumferential scanning laser fuze using nonlinear adaptive backscatter filter based on pauseable SAF-LMS algorithm

The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm.This enhances the algorithm loading capability of the fuze.In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)algorithm.We also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the fuze.Finally,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals.The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance.

MCWOA Scheduler:Modified Chimp-Whale Optimization Algorithm for Task Scheduling in Cloud Computing

Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).

Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

Rao Algorithms-Based Structure Optimization for Heterogeneous Wireless Sensor Networks

The structural optimization of wireless sensor networks is a critical issue because it impacts energy consumption and hence the network’s lifetime.Many studies have been conducted for homogeneous networks,but few have been performed for heterogeneouswireless sensor networks.This paper utilizes Rao algorithms to optimize the structure of heterogeneous wireless sensor networks according to node locations and their initial energies.The proposed algorithms lack algorithm-specific parameters and metaphorical connotations.The proposed algorithms examine the search space based on the relations of the population with the best,worst,and randomly assigned solutions.The proposed algorithms can be evaluated using any routing protocol,however,we have chosen the well-known routing protocols in the literature:Low Energy Adaptive Clustering Hierarchy(LEACH),Power-Efficient Gathering in Sensor Information Systems(PEAGSIS),Partitioned-based Energy-efficient LEACH(PE-LEACH),and the Power-Efficient Gathering in Sensor Information Systems Neural Network(PEAGSIS-NN)recent routing protocol.We compare our optimized method with the Jaya,the Particle Swarm Optimization-based Energy Efficient Clustering(PSO-EEC)protocol,and the hybrid Harmony Search Algorithm and PSO(HSA-PSO)algorithms.The efficiencies of our proposed algorithms are evaluated by conducting experiments in terms of the network lifetime(first dead node,half dead nodes,and last dead node),energy consumption,packets to cluster head,and packets to the base station.The experimental results were compared with those obtained using the Jaya optimization algorithm.The proposed algorithms exhibited the best performance.The proposed approach successfully prolongs the network lifetime by 71% for the PEAGSIS protocol,51% for the LEACH protocol,10% for the PE-LEACH protocol,and 73% for the PEGSIS-NN protocol;Moreover,it enhances other criteria such as energy conservation,fitness convergence,packets to cluster head,and packets to the base station.

Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection

In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate.Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter,but the results obtained depend on the value of the parameter.To eliminate this parameter’s influence,the problem can be reformulated as a multi-objective optimization problem.The Whale Optimization Algorithm(WOA)is widely used in optimization problems because of its simplicity and easy implementation.In this paper,we propose a multi-strategy assisted multi-objective WOA(MSMOWOA)to address feature selection.To enhance the algorithm’s search ability,we integrate multiple strategies such as Levy flight,Grey Wolf Optimizer,and adaptive mutation into it.Additionally,we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain diversity.Results on fourteen University of California Irvine(UCI)datasets demonstrate that our proposed method effectively removes redundant features and improves classification performance.The source code can be accessed from the website:https://github.com/zc0315/MSMOWOA.

Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data.The communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time.Therefore,the scheme of an effectual routing protocol for reliable and stable communications is significant.Current research demonstrates that clustering is an intelligent method for effectual routing in a mobile environment.Therefore,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in VANETS.The FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the VANET.To accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust level.For the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR method.The experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

中国式现代化语境下主流节目IP创设的询唤机制解析——以“家国young貌”系列节目为例

在Web3.0时代,数字信息技术为主流节目IP的交互共享提供了技术支持,但节目内容的泛娱乐化倾向导致部分主流节目叙事传播不畅。文章以中央广播电视总台2023年系列节目“家国young貌”为例,剖析当代主流节目的破圈实践,并结合询唤理论,从话语下沉、仪式沉浸、知行规训三方面提出主流节目IP创设对培植文化自信、建构精神谱系的作用机制。

Improvement of High-Speed Detection Algorithm for Nonwoven Material Defects Based on Machine Vision

Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.

Genetic algorithm assisted meta-atom design for high-performance metasurface optics

Metasurfaces,composed of planar arrays of intricately designed meta-atom structures,possess remarkable capabilities in controlling electromagnetic waves in various ways.A critical aspect of metasurface design involves selecting suitable meta-atoms to achieve target functionalities such as phase retardation,amplitude modulation,and polarization conversion.Conventional design processes often involve extensive parameter sweeping,a laborious and computationally intensive task heavily reliant on designer expertise and judgement.Here,we present an efficient genetic algorithm assisted meta-atom optimization method for high-performance metasurface optics,which is compatible to both single-and multiobjective device design tasks.We first employ the method for a single-objective design task and implement a high-efficiency Pancharatnam-Berry phase based metalens with an average focusing efficiency exceeding 80%in the visible spectrum.We then employ the method for a dual-objective metasurface design task and construct an efficient spin-multiplexed structural beam generator.The device is capable of generating zeroth-order and first-order Bessel beams respectively under right-handed and left-handed circular polarized illumination,with associated generation efficiencies surpassing 88%.Finally,we implement a wavelength and spin co-multiplexed four-channel metahologram capable of projecting two spin-multiplexed holographic images under each operational wavelength,with efficiencies over 50%.Our work offers a streamlined and easy-to-implement approach to meta-atom design and optimization,empowering designers to create diverse high-performance and multifunctional metasurface optics.

Product quality prediction based on RBF optimized by firefly algorithm

With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.

Path Planning for AUVs Based on Improved APF-AC Algorithm

With the increase in ocean exploration activities and underwater development,the autonomous underwater vehicle(AUV)has been widely used as a type of underwater automation equipment in the detection of underwater environments.However,nowadays AUVs generally have drawbacks such as weak endurance,low intelligence,and poor detection ability.The research and implementation of path-planning methods are the premise of AUVs to achieve actual tasks.To improve the underwater operation ability of the AUV,this paper studies the typical problems of path-planning for the ant colony algorithm and the artificial potential field algorithm.In response to the limitations of a single algorithm,an optimization scheme is proposed to improve the artificial potential field ant colony(APF-AC)algorithm.Compared with traditional ant colony and comparative algorithms,the APF-AC reduced the path length by 1.57%and 0.63%(in the simple environment),8.92%and 3.46%(in the complex environment).The iteration time has been reduced by approximately 28.48%and 18.05%(in the simple environment),18.53%and 9.24%(in the complex environment).Finally,the improved APF-AC algorithm has been validated on the AUV platform,and the experiment is consistent with the simulation.Improved APF-AC algorithm can effectively reduce the underwater operation time and overall power consumption of the AUV,and shows a higher safety.