融合差分进化和Sine混沌的改进粒子群算法

将差分进化与Sine混沌相结合,提出一种改进的粒子群算法。利用Sine混沌映射对初始种群进行优化,提高了收敛速度;该算法通过引入非同步变化的学习因子的速度更新公式,引入随机惯性权重,使算法能够更好地兼顾全局搜索与局部优化;借鉴差分进化算法中的交叉操作,采用淘汰机制随机搜索策略,提高算法的全局搜索能力,提高算法收敛速度。为了验证融合差分进化和Sine混沌的改进粒子群算法(improved particle swarm optimization algorithm,IPSO)的性能,与基于压缩学习因子的粒子群算法(yield-based particle swarm optimization,YPSO)、自适应加权粒子群算法(self-adaptive particle swarm optimization,SPSO)等PSO相关算法以及蜘蛛蜂优化算法(spider wasp optimization,SWO)、能量谷算法(energy valley algorithm,EVA)等2023年最新算法相比较,验证融合差分进化和Sine混沌的改进粒子群算法(IPSO)的有效性。在不同维度下解决12个常用基准函数,对12个测试函数进行实验,并与其他的几种算法进行比较,实验结果表明,改进后的PSO算法收敛速度快,收敛精度高。

基于耦合的Henon和Sine映射及彩色图像加密

针对现有混沌映射模型的混沌空间小及混沌能力弱的问题,通过耦合二维Henon混沌映射模型和Sine混沌映射模型,设计一种新的二维混沌映射模型,通过耦合Sine混沌映射模型和Logistic混沌映射模型,设计一种新的一维混沌映射模型。针对使用单一置乱方法进行图像加密时安全性能不高的问题,通过使用Lorenz映射设计一种随机选择置乱算法的方法。使用设计的两种混沌映射及置乱选择方法,提出一种彩色图像加密方案。仿真实验结果表明:所提方法具有较大的置乱范围、较高的安全性和良好的图像加密效果,对常见的攻击有较强的抵抗力,且易于实现。

STARLIKENESS ASSOCIATED WITH THE SINE HYPERBOLIC FUNCTION

Let q_(λ)(z)=1+λsinh(ζ),0<λ<1/sinh(1)be a non-vanishing analytic function in the open unit disk.We introduce a subclass S^(*)(q_(λ))of starlike functions which contains the functions f such that zf'/f is subordinated by q_(λ).We establish inclusion and radii results for the class S^(*)(q_(λ))for several known classes of starlike functions.Furthermore,we obtain sharp coefficient bounds and sharp Hankel determinants of order two for the class S^(*)(q_(λ)).We also find a sharp bound for the third Hankel determinant for the caseλ=1/2.

Bayesian and Non-Bayesian Analysis for the Sine Generalized Linear Exponential Model under Progressively Censored Data

This article introduces a novel variant of the generalized linear exponential(GLE)distribution,known as the sine generalized linear exponential(SGLE)distribution.The SGLE distribution utilizes the sine transformation to enhance its capabilities.The updated distribution is very adaptable and may be efficiently used in the modeling of survival data and dependability issues.The suggested model incorporates a hazard rate function(HRF)that may display a rising,J-shaped,or bathtub form,depending on its unique characteristics.This model includes many well-known lifespan distributions as separate sub-models.The suggested model is accompanied with a range of statistical features.The model parameters are examined using the techniques of maximum likelihood and Bayesian estimation using progressively censored data.In order to evaluate the effectiveness of these techniques,we provide a set of simulated data for testing purposes.The relevance of the newly presented model is shown via two real-world dataset applications,highlighting its superiority over other respected similar models.

Sine oculis homeobox homolog family function in gastrointestinal cancer:Progression and comprehensive analysis

The sine oculis homeobox homolog(SIX)family,a group of transcription factors characterized by a conserved DNA-binding homology domain,plays a critical role in orchestrating embryonic development and organogenesis across various organisms,including humans.Comprising six distinct members,from SIX1 to SIX6,each member contributes uniquely to the development and differentiation of diverse tissues and organs,underscoring the versatility of the SIX family.Dysregulation or mutations in SIX genes have been implicated in a spectrum of developmental disorders,as well as in tumor initiation and progression,highlighting their pivotal role in maintaining normal developmental trajectories and cellular functions.Efforts to target the transcriptional complex of the SIX gene family have emerged as a promising strategy to inhibit tumor development.While the development of inhibitors targeting this gene family is still in its early stages,the significant potential of such interventions holds promise for future therapeutic advances.Therefore,this review aimed to comprehensively explore the advancements in understanding the SIX family within gastrointestinal cancers,focusing on its critical role in normal organ development and its implications in gastrointestinal cancers,including gastric,pancreatic,colorectal cancer,and hepatocellular carcinomas.In conclusion,this review deepened the understanding of the functional roles of the SIX family and explored the potential of utilizing this gene family for the diagnosis,prognosis,and treatment of gastrointestinal cancers.

无界区域上耦合sine-Gordon方程组的分裂局部人工边界条件

研究了在等离子体物理学中有广泛应用的无界区域上耦合sine-Gordon方程组的数值解法,由于物理区域的无界性和方程组的非线性,使得常用的数值方法不能直接用于求解此问题。利用人工边界方法和算子分裂方法设计了合理的分裂局部人工边界条件,解决了物理区域的无界性和方程组的非线性给数值计算带来的困难。无界区域上的Cauchy问题简化为有界区域上的初边值问题,从而可以利用有限差分方法进行数值求解。最后,通过数值算例验证了所设计边界条件的精确性和有效性,并模拟了多孤立波的传播。

非线性sine-Gordon方程的连续时空混合有限元方法

该文将混合有限元方法和连续时空有限元方法相结合,构造了sine-Gordon方程的连续时空混合有限元离散格式,引入独立变量p=u_(t)来求解,并将时间变量和空间变量都用有限元方法处理.此格式可以将方程降阶,降低有限元空间的光滑性要求,同时在时间和空间两个方向都能发挥有限元方法的优势,获得时空高精度的数值解.理论分析中严格证明了数值解的稳定性,给出了u和p的误差估计.最后通过数值算例的结果展示了格式的有效性和可行性.

Utilization of Logistical Regression to the Modified Sine-Gordon Model in the MST Experiment

In this paper, a logistical regression statistical analysis (LR) is presented for a set of variables used in experimental measurements in reversed field pinch (RFP) machines, commonly known as “slinky mode” (SM), observed to travel around the torus in Madison Symmetric Torus (MST). The LR analysis is used to utilize the modified Sine-Gordon dynamic equation model to predict with high confidence whether the slinky mode will lock or not lock when compared to the experimentally measured motion of the slinky mode. It is observed that under certain conditions, the slinky mode “locks” at or near the intersection of poloidal and/or toroidal gaps in MST. However, locked mode cease to travel around the torus;while unlocked mode keeps traveling without a change in the energy, making it hard to determine an exact set of conditions to predict locking/unlocking behaviour. The significant key model parameters determined by LR analysis are shown to improve the Sine-Gordon model’s ability to determine the locking/unlocking of magnetohydrodyamic (MHD) modes. The LR analysis of measured variables provides high confidence in anticipating locking versus unlocking of slinky mode proven by relational comparisons between simulations and the experimentally measured motion of the slinky mode in MST.

基于SINE-RIPs标记的苏姜猪群体结构和遗传多样性分析

为了探究苏姜猪群体遗传结构与遗传多样性,本研究采用36个SINE-RIPs(SINE Retrotransposon Insertion Polymorphisms)标记检测苏姜猪育种核心群体的基因多态性,并分析群体的遗传参数,构建UPGMA(Unweightedpair Group Method Arithmeticmean)系统发育树。结果发现,苏姜猪群体中存在29个SINE-RIPs多态性,UPGMA系统发育树将苏姜猪群体分为10个家系,家系的PIC范围为0.1883~0.2371,Fst的范围为0.011 7~0.299 1,Fis的范围为-0.902 0~-0.049 3,说明苏姜猪群体存在丰富的遗传多样性,群体近交程度较低。该研究结果为苏姜猪的持续选育和开发利用提供参考依据。

Algorithm for Smooth S-curve Feedrate Profiling Generation

The algorithms for feedrate profile generation,such as linear and S-curve profiles,have been widely used in machinery controllers,and these algorithms can greatly improve the smoothness of motion.However,most of the algorithms lead to the discontinuous acceleration/deceleration and jerk,or high jerk levels,which is very harmful to machine tool or robot in most occasions. This paper presents a smooth S-curve feedrate profiling generation algorithm that produces continuous feedrate,acceleration,and jerk profiles.Smooth jerk is obtained by imposing limits on the first and second time derivatives of acceleration,resulting in trapezoidal jerk profiles along the tool path.The discretization of smooth S-curve feedrate is realized with a novel approach that improves the efficiency without calculating the deceleration point in each sampled time.To ensure that the interpolation time is a multiple of the value of sampled time,the feedrate,acceleration,jerk,and jerk derivative are recalculated.Meantime,to improve the efficiency,the interpolation steps of all regions are computed before interpolation.According to the distance of trajectory,the smooth S-curve acceleration and decelerations are divided into three blocks：normal block,short block type-Ⅰ,and short block type-Ⅱ.Finally feedrate discretization of short block type-Ⅰand type-Ⅱis obtained with considering the efficiency.The proposed generation algorithm is tested in machining a part on a five axis milling machine,which is controlled with the CNC system for newly developed high-speed machine tools.The test result shows that the smooth S-curve approach has the smoother feedrate,acceleration,deceleration,and jerk profiles than S-curve.The proposed algorithm ensures the automated machinery motion smoothness,and improves the quality and efficiency of the automated machinery motion planning.

Sine-Gordon方程二重网格方法的高精度分析

讨论了Sine-Gordon方程的二重网格有限元方法,建立了EQ_(1)^(rot)非协调元全离散逼近格式.直接利用EQ_(1)^(rot)元的插值算子和该元的性质,并对非线性项进行线性化处理,导出了能量模意义下的超逼近结果.更进一步利用插值后处理技术得到整体超收敛结果.

基于Logistic-Sine-Cosine映射的图像加密算法

为了提高数字图像加密的速度和算法的安全性,提出了基于Fridrich框架的加密算法。通过Fisher-Yates变换置乱原始图像打破像素间的强相关性,再利用滤波器在RGB平面上进行滤波得到密文。Logistic-Sine-Cosine复合混沌系统具有良好的混沌特性,且时间复杂度低。密钥由输入参数和明文的SHA-512值共同决定,对明文高度敏感。二维滤波器扩散效果良好,其滤波器模板由明文和密钥决定并引入了伪随机像素值,在提升扩散效果的同时增强了系统抗差分攻击的能力。仿真结果表明,密文图像像素分布近似均匀、像素关联性弱、密钥空间足够大、密钥敏感性高,能够有效抵抗暴力、裁剪、差分等常见攻击,具有较高的安全性,且算法时间复杂度较低。

Sine Cosine Optimization with Deep Learning-Based Applied Linguistics for Sentiment Analysis on COVID-19 Tweets

Applied linguistics is an interdisciplinary domain which identifies,investigates,and offers solutions to language-related real-life problems.The new coronavirus disease,otherwise known as Coronavirus disease(COVID-19),has severely affected the everyday life of people all over the world.Specifically,since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection,the country has initiated the appropriate preventive measures(like lockdown,physical separation,and masking)for combating this extremely transmittable disease.So,individuals spent more time on online social media platforms(i.e.,Twitter,Facebook,Instagram,LinkedIn,and Reddit)and expressed their thoughts and feelings about coronavirus infection.Twitter has become one of the popular social media platforms and allows anyone to post tweets.This study proposes a sine cosine optimization with bidirectional gated recurrent unit-based senti-ment analysis(SCOBGRU-SA)on COVID-19 tweets.The SCOBGRU-SA technique aimed to detect and classify the various sentiments in Twitter data during the COVID-19 pandemic.The SCOBGRU-SA technique follows data pre-processing and the Fast-Text word embedding process to accomplish this.Moreover,the BGRU model is utilized to recognise and classify sen-timents present in the tweets.Furthermore,the SCO algorithm is exploited for tuning the BGRU method’s hyperparameter,which helps attain improved classification performance.The experimental validation of the SCOBGRU-SA technique takes place using a benchmark dataset,and the results signify its promising performance compared to other DL models.

A Double Adaptive Random Spare Reinforced Sine Cosine Algorithm

Many complex optimization problems in the real world can easily fall into local optimality and fail to find the optimal solution,so more new techniques and methods are needed to solve such challenges.Metaheuristic algorithms have received a lot of attention in recent years because of their efficient performance and simple structure.Sine Cosine Algorithm(SCA)is a recent Metaheuristic algorithm that is based on two trigonometric functions Sine&Cosine.However,like all other metaheuristic algorithms,SCA has a slow convergence and may fail in sub-optimal regions.In this study,an enhanced version of SCA named RDSCA is suggested that depends on two techniques:random spare/replacement and double adaptive weight.The first technique is employed in SCA to speed the convergence whereas the second method is used to enhance exploratory searching capabilities.To evaluate RDSCA,30 functions from CEC 2017 and 4 real-world engineering problems are used.Moreover,a nonparametric test called Wilcoxon signed-rank is carried out at 5%level to evaluate the significance of the obtained results between RDSCA and the other 5 variants of SCA.The results show that RDSCA has competitive results with other metaheuristics algorithms.

Shape and Size Optimization of Truss Structures under Frequency Constraints Based on Hybrid Sine Cosine Firefly Algorithm

Shape and size optimization with frequency constraints is a highly nonlinear problem withmixed design variables,non-convex search space,and multiple local optima.Therefore,a hybrid sine cosine firefly algorithm(HSCFA)is proposed to acquire more accurate solutions with less finite element analysis.The full attraction model of firefly algorithm(FA)is analyzed,and the factors that affect its computational efficiency and accuracy are revealed.A modified FA with simplified attraction model and adaptive parameter of sine cosine algorithm(SCA)is proposed to reduce the computational complexity and enhance the convergence rate.Then,the population is classified,and different populations are updated by modified FA and SCA respectively.Besides,the random search strategy based on Lévy flight is adopted to update the stagnant or infeasible solutions to enhance the population diversity.Elitist selection technique is applied to save the promising solutions and further improve the convergence rate.Moreover,the adaptive penalty function is employed to deal with the constraints.Finally,the performance of HSCFA is demonstrated through the numerical examples with nonstructural masses and frequency constraints.The results show that HSCFA is an efficient and competitive tool for shape and size optimization problems with frequency constraints.

正余弦函数法结合sine-Gordon方程的解求BBM方程的新行波解

为研究BBM方程的解析解法和行波解。首先用试探函数法获得了sine-Gordon方程及约化方程的精确解,然后采用sine-Gordon方程的约化方程和精确解构造了正余弦函数法,最后利用正余弦函数法找到了BBM方程的许多新显式行波解。

Etiologies and Associated Factors of Generalised Pruritus Sine Materia in the Dermatology-Venerology Department of CHUD-Borgou/Alibori (Benin)

Introduction: The aim was to identify the etiologies of generalised pruritus sine materia and to determine the associated factors in Parakou. Methods: This was a retrospective observational study conducted from January 2011 to June 2022. The patients included were of all ages and both sexes in whom the sine materia nature of the pruritus was noted after clinical examination. These patients had an etiological assessment available or not, complete or partial. For each patient, socio-demographic, socio-cultural, socio-economic and clinical data were collected using a pre-established survey form. They were then processed and analysed using Epi Data 3.1 and SPSS version 21 software respectively. Results: The incidence of generalised pruritus sine materia was 0.89% (73 cases/8214 consultants). The predominant etiologies were aquagenic pruritus (16.4%) and intestinal parasitosis (12.3%). After a bi-variate analysis, two risk factors were identified: frequency of towel change greater than 1 month (OR = 3.02;CI95% = 0.98 - 9.31;P = 0.0486) and use of cold water for bath (OR = 3.28;CI95% = 1.09 - 9.81;P = 0.0274). Conclusion: The etiologies and associated factors of generalised pruritus sine materia found in Parakou are varied but are linked to lifestyle. There is an urgent need to raise public awareness of the need to improve lifestyle in order to reduce the frequency of pruritus sine materia. .

基于正弦函数拟合的高动态捷联惯导姿态更新算法

为提高捷联惯导在高动态条件下的姿态解算精度,基于等效旋转矢量泰勒级数展开法,提出一种基于正弦函数拟合的高动态捷联惯导姿态更新算法。以正弦函数拟合载体运动角速度,考虑Bortz方程高阶项的影响,对陀螺角增量表示的旋转矢量进行泰勒六阶展开,对比旋转矢量不同形式表达式求得误差补偿系数。在MATLAB平台上,以圆锥运动与大角速率转动并存环境作为仿真条件,对所提算法与传统算法进行对比仿真分析。仿真结果表明,在小半锥角低频圆锥运动伴随高速角速率转动情况下,所提算法性能较好,当半锥角为0.5°、角频率为2.26πrad/s、常值角速率为5.30 rad/s、姿态解算周期为0.02 s时,所提正弦函数拟合三子样旋转矢量算法与传统扩展形式频率级数/显示频率三子样圆锥算法相比误差降低了2个数量级。

基于多策略优化的三维无线传感器网络定位算法

在经典三维无线传感器网络定位算法中,极大似然估计法定位存在矩阵无法求逆的问题。针对此情况,提出了一种基于多策略优化的三维无线传感器网络定位算法。首先,引入Sine映射对金枪鱼群初始种群进行混沌映射,增强了初始种群的多样性及均匀性。其次,结合非线性收敛因子和自适应权重策略对金枪鱼每次位置迭代更新进行优化,避免算法陷入局部最优,进一步提升算法的搜索速度和寻优的准确性。最后,采用多策略增强金枪鱼群优化算法在三维空间中对每个未知节点位置进行计算,解决了矩阵无法求逆的情况,有效降低了待定位节点位置的计算误差。实验结果表明:新提出的定位算法、经典三维定位算法、三维加权DV-Hop定位算法与灰狼优化的三维定位算法平均定位误差分别为13%、73%、30%和17%。

新工科背景下通信电子线路基础实验课程改革探索

针对目前通信电子线路实验内容设置单一、教学手段不够丰富、教学效果不够理想等问题,提出了以任务为主线、以教师为主导、以学生为主体的实验模式,并融入联想、启发、任务驱动、项目类比等多种教学方法。以高稳定度LC正弦波振荡器的设计为例,设计了“基本”“进阶”“扩展”3种实验任务与内容,以多样化“套餐”满足学生对于实验的不同需求;在一块电路板上实现克拉泼、西勒和并联型晶体振荡器电路,使学生更容易掌握3种电路的区别与联系;采用了项目类比教学法,通过本实验使学生掌握解决典型通信工程问题的流程与科学方法;任务引入与思政环节巧妙结合,树立学生科学兴国的意识,增强民族自豪感。