Butterfly taxonomic and functional diversity in the urban green spaces of Hefei city

Urbanization has profound impacts on ecological environments. Green spaces are a vital component of urban ecosystems and play a crucial role in maintaining ecological balance and enhancing sustainability. This study aimed to investigate the community composition characteristics of butterflies in urban green spaces within the context of rapid urbanization. Simultaneously, it explored the status and differences in butterfly taxonomic diversity, functional diversity, and functional traits among different types of urban green spaces, regions, and urban gradients to provide relevant insights for further improving urban green space quality and promoting biodiversity conservation. We conducted a year-long survey of 80 green spaces across different urban regions and ring roads within Hefei City, Anhui Province, with monthly sampling intervals over 187 transects. A total of 4822 butterflies, belonging to 5 families, 17 subfamilies, 40 genera, and 55 species were identified. The species richness, Shannon, Simpson, functional richness, and Rao's quadratic entropy indices of butterflies in urban park green spaces were all significantly higher than those in residential and street green spaces(P < 0.05). Differences in butterfly diversity and functional traits among different urban regions and ring roads were relatively minor, and small-sized, multivoltine, and long flying duration butterflies dominated urban green spaces. Overall, these spaces offer more favorable habitats for butterflies. However, some residential green spaces and street green spaces demonstrate potential for butterfly conservation.

Lack of Evidence for Local Adaptation of the Endangered Karner Blue Butterfly to Its Sole Larval Hostplant—The Wild Lupine

Local adaptation is an important process that drives the evolution of populations within species, and it can be generally expressed by the higher fitness of individuals raised in their native habitats versus in a foreign location. The influence of local adaptation is especially prominent in species that subsist in small and/or highly isolated populations. This study evaluated whether the federally endangered Karner blue butterfly, Lycaeides melissa samuelis (Lepidoptera: Lycaenidae) is locally adapted to its exclusive larval host plant, the wild lupine (Lupinus perennis). To test for local adaptation, individuals from a laboratory-raised colony were reared on wild lupine plants from populations belonging to either their native (Indiana) or a foreign (Michigan and Wisconsin) region. For this purpose, lupine plants from the different populations were grown in a common garden in growth chambers, and one Karner blue larva was placed on each plant. Fitness traits related to growth and development were recorded for each butterfly across populations. Days from hatching to pupation and eclosion showed gender-specific significant differences across wild lupine populations and plant genotypes (within populations). The percent survival of butterflies (from hatching to eclosion) also differed among plants from different populations. These results indicate that wild lupine sources can affect some developmental traits of Karner blue butterflies. However, growth-related traits, such as pupal and adult weight of individuals reared in plants from native populations did not differ from those of foreign regions. The apparent absence of local adaptation to wild lupine suggests that, at least, some individuals of this species could be translocated from native populations to foreign reintroduction sites without experiencing decreased fitness levels. However, future studies including more populations across the geographical range of this butterfly are recommended to evaluate other environmental factors that could influence adaptation on a wider spatial scale.

Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic Butterfly Optimization with Deep Learning Model

Autism spectrum disorder(ASD)can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics,like changes in behavior,social disabilities,and difficulty communicating with others.Eye tracking(ET)has become a useful method to detect ASD.One vital aspect of moral erudition is the aptitude to have common visual attention.The eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early detection.Eye-tracking data can offer insightful information about the behavior and thought processes of people with ASD,but it is important to be aware of its limitations and to combine it with other types of data and assessment techniques to increase the precision of ASD detection.It operates by scanning the paths of eyes for extracting a series of eye projection points on images for examining the behavior of children with autism.The purpose of this research is to use deep learning to identify autistic disorders based on eye tracking.The Chaotic Butterfly Optimization technique is used to identify this specific disturbance.Therefore,this study develops an ET-based Autism Spectrum Disorder Diagnosis using Chaotic Butterfly Optimization with Deep Learning(ETASD-CBODL)technique.The presented ETASDCBODL technique mainly focuses on the recognition of ASD via the ET and DL models.To accomplish this,the ETASD-CBODL technique exploits the U-Net segmentation technique to recognize interested AREASS.In addition,the ETASD-CBODL technique employs Inception v3 feature extraction with CBO algorithm-based hyperparameter optimization.Finally,the long-shorttermmemory(LSTM)model is exploited for the recognition and classification of ASD.To assess the performance of the ETASD-CBODL technique,a series of simulations were performed on datasets from the figure-shared data repository.The experimental values of accuracy(99.29%),precision(98.78%),sensitivity(99.29%)and specificity(99.29%)showed a better performance in the ETASD-CBODL technique over recent approaches.

Butterfly Optimized Feature Selection with Fuzzy C-Means Classifier for Thyroid Prediction

The main task of thyroid hormones is controlling the metabolism rate of humans,the development of neurons,and the significant growth of reproductive activities.In medical science,thyroid disorder will lead to creating thyroiditis and thyroid cancer.The two main thyroid disorders are hyperthyroidism and hypothyroidism.Many research works focus on the prediction of thyroid disorder.To improve the accuracy in the classification of thyroid disorder this paper pro-poses optimization-based feature selection by using differential evolution with the Butterfly optimization algorithm(DE-BOA).For the classifier fuzzy C-means algorithm(FCM)is used.The proposed DEBOA-FCM is evaluated with para-metric metric measures of sensitivity,specificity,and accuracy.In this work,the thyroid disease dataset collected from the machine learning University of Cali-fornia Irvine(UCI)database was used.The accuracy rate for the Differential Evo-lutionary algorithm got 0.884,the Butterfly optimization algorithm got 0.906,Fuzzy C-Means algorithm got 0.899 and DEBOA+Focused Concept Miner(FCM)proposed work 0.943.

Adaptive Butterfly Optimization Algorithm(ABOA)Based Feature Selection and Deep Neural Network(DNN)for Detection of Distributed Denial-of-Service(DDoS)Attacks in Cloud

Cloud computing technology provides flexible,on-demand,and completely controlled computing resources and services are highly desirable.Despite this,with its distributed and dynamic nature and shortcomings in virtualization deployment,the cloud environment is exposed to a wide variety of cyber-attacks and security difficulties.The Intrusion Detection System(IDS)is a specialized security tool that network professionals use for the safety and security of the networks against attacks launched from various sources.DDoS attacks are becoming more frequent and powerful,and their attack pathways are continually changing,which requiring the development of new detection methods.Here the purpose of the study is to improve detection accuracy.Feature Selection(FS)is critical.At the same time,the IDS’s computational problem is limited by focusing on the most relevant elements,and its performance and accuracy increase.In this research work,the suggested Adaptive butterfly optimization algorithm(ABOA)framework is used to assess the effectiveness of a reduced feature subset during the feature selection phase,that was motivated by this motive Candidates.Accurate classification is not compromised by using an ABOA technique.The design of Deep Neural Networks(DNN)has simplified the categorization of network traffic into normal and DDoS threat traffic.DNN’s parameters can be finetuned to detect DDoS attacks better using specially built algorithms.Reduced reconstruction error,no exploding or vanishing gradients,and reduced network are all benefits of the changes outlined in this paper.When it comes to performance criteria like accuracy,precision,recall,and F1-Score are the performance measures that show the suggested architecture outperforms the other existing approaches.Hence the proposed ABOA+DNN is an excellent method for obtaining accurate predictions,with an improved accuracy rate of 99.05%compared to other existing approaches.

Construction of the “Madame Butterfly Fantasy” in M.Butterfly

M.Butterfly,written by David Henry Hwang,is one of the most influential plays in contemporary American dramatic his-tory.Hwang incorporates a stereotypical fantasy into his play,involving conflicts and misunderstandings between the East and theWest,between men and women.Through the analysis of the construction of the Orientalist"Madame Butterfly Fantasy"in M.Butterfly,this paper analyzes sexual prejudice and racial bias embedded in the"Fantasy".

Butterfly网络下移数与p序置换统一架构研究和实现

为避免目前移数置换和p序置换实现方式资源利用率低、灵活性差的问题,研究Butterfly网络的特点,设计并实现基于Butterfly网络的移数置换和p序置换统一硬件架构。对架构的路由算法进行分析和提取,对路由信息生成电路进行专门的设计和优化,支持路由信息的实时生成。对移数置换和p序置换进行功能仿真和性能评估,仿真结果表明,和同类设计相比,该架构路由算法简单、适配方便,且支持多路并行操作,增强了电路灵活性,提高了资源利用率。

基于Inverse Butterfly/Butterfly网络的置换-逆序与置换-移位选路算法

本文利用Inverse Butterfly/Butterfly多级动态互连网络的自路由和可重排特性,提出了基于该网络的置换-逆序和置换-移位选路算法.它们都能够对所有一次通过该网络的任意置换结果动态地完成逆序和移位操作,且算法复杂度低,硬件实现简洁.进一步,将本文提出的算法对基于该网络设计的置换操作进行了功能扩展,分别构建了置换-逆序、置换-移位以及置换-逆序-移位硬件单元.并将它们在SMIC 65nm工艺下进行了综合,结果表明:当在以往研究成果上扩展逆序操作时,硬件电路面积仅增加约6%且几乎不影响原架构的延迟;当在以往研究成果上扩展移位和逆序-移位操作时,原架构以18%和21%的面积增加值和30%的延迟增加值,实现了功能性2倍的提升.

基于Butterfly算法的大容量储能系统成组技术

大容量储能系统(large capacity storage system,LCSS)的不同成组方式直接影响LCSS的内阻特性,目前针对LCSS成组方式的研究还处于起步阶段,本工作基于大容量液流电池储能系统(large capacity vanadium redox battery storage system,LCVRBSS)进行研究,通过分析单电堆不同成组方式的内阻特性,选择蝴蝶算法(butterfly algorithm)对不同成组方式在不同荷电状态(state of charge,SOC)下的内阻特性进行曲面拟合,内阻的最大拟合误差控制在3.6%以内。并且以单电堆内阻最小为优化目标,使用粒子群算法进行寻优,得到单电堆最佳成组方式。

基于Butterfly细分规则的虚拟作物DFFD形态建模

为了增强虚拟作物环境,提出了一种基于Butterfly细分规则以及DFFD方法的虚拟作物建模方法。根据植株的形态结构特征选取相应的初始叶片模型,用Butterfly插值型细分方法对其细分,以确定控制点集。通过扩充已有DFFD法对三维植株整体特征进行模拟,实现了小麦叶片向水稻植株的DFFD形态建模。实验证明,该方法较为逼真地虚拟作物植株建模效果,能够满足虚拟作物环境的需要。

基于改进型Butterfly细分的三角网格自由曲面环形刀具轨迹规划

针对刀具轨迹连续性差、切削刀具负荷波动大的问题,提出一种基于改进型Butterfly细分的三角网格曲面环形刀具轨迹规划方法。以三角网格曲面的几何特征为基础构建曲面定点数据表达结构,结合环形刀的几何特征对刀触点(CC点)的刀具曲面及工件曲面进行几何分析,构建三角网格曲面环形刀切削模型。根据切削刀具干涉原理计算刀具的最小前倾角以获得切削路径的最大宽度,并据此采用改进型Butterfly细分方法对三角网格曲面进行细分,使刀具轨迹的行距达到切削路径宽度的要求。以曲面轮廓曲线为初始刀具轨迹,沿着三角网格边界进行螺旋型刀具轨迹刀触点(CC点)的逐行规划,最终根据环形刀几何模型求解刀心(CL点)位置。根据选择的刀具参数和给定的残余高度要求,仿真生成的三角网格曲面螺旋刀具轨迹的连续性好,从而降低了切削刀具负载的波动,最后通过机加工实验验证了刀具轨迹的可行性。

改良Stoppa切口与双侧Letournel切口在骨盆Butterfly骨折内固定中应用的比较分析

目的:通过分析比较改良Stoppa切口与双侧Letournel切口在骨盆Butterfly骨折内固定中应用的不同结果,评价改良Stoppa切口的在该类骨折中的应用价值。方法:分析自2009年8月至2012年2月间,我院经改良Stoppa切口行骨盆Butterfly骨折的10例病例的手术时间、出血量。并与采用双侧Letournel切口组6例病例进行比较。结果:2组病例在手术时间(t=8.38P<0.01)、术中失血量(t=6.11P<0.01),存在差异。结论:经改良Stoppa切口行骨盆Butterfly骨折内固定,具有手术时间短、术中出血少,操作便捷、并发症少,骨折复位精确的特点,是该骨折手术治疗的首选方式。

Butterfly细分曲面的快速求值

从分析Butterfly细分曲面的局部参数化出发,通过参数二进制分解生成数列以及构造细分格式的局部加细矩阵,并利用对应矩阵序列乘积与矩阵特征分解计算出控制点权值,从而解决了B utterfly细分曲面插值问题。算法独立于网格存储的数据结构,避免了求邻接关系等费时的操作,并且容易推广到其他三角网格插值细分格式的曲面求值中。

On the Main Characters in“Days of the Butterfly”

"Days of the Butterfly"was one of Alice Munro's short stories written in 1950 s, when she was still a new hand. This paper will mainly focus on the development of the main characters in the story, including Myra, the black girl who was the heroine, the teacher Miss Darling, and the Narrator"I". In portraying these characters, Munro adopted several writing techniques including conversation, detailed description, psychological description and so on. She vividly transferred those characters into paper.

基于Butterfly网络的移数和p序置换统一架构研究与实现

为有效解决目前移数置换和p序置换硬件实现方式并行性和灵活性差、功能扩展性不强的问题,研究了Butterfly网络的特点,设计并实现了基于Butterfly网络的移数和p序置换的统一架构,分析并提取出支持该架构的路由算法。与传统对数移位器和桶形移位器相比,本架构并行性更好,灵活性更高,功能扩展性更强,同时支持短字置换和多路并行操作。与I-BFLY移位器相比,架构面积节省了30.0%且速度提升了17.6%。

准butterfly型四核铜配合物磁性质的计算模拟

对四核铜化合物[Cu_4(dpk·CH_3O)_4(CH_3O)_2]·(ClO_4)_2的变温磁化率和5K温度下磁化强度的实验数据进行理论模拟计算.结果表明,处于这种结构下的四核铜交换机理,用准butterfly模型描述比用butterfly模型更具有明确的化学和物理意义。

Occurrence Law of Cabbage Butterfly in China and Its Identification and Prevention

Cabbage butterfly occurs throughout the world, which mainly causes damage on cruciferae vegetables. The morphology characteristic of cabbage butterfly is introduced, which is also compared with the morphology characteristic of the other insects such as cabbage moth （Plutella maculipennis）, cabbage webworm （Hellula undalis Fabricius）. The occurrence law, living habit and selection of prevention pesticides against cabbage butterfly in China are summarized, which will provide the basic information of cabbage butterfly in China for worldwide further research on cabbage butterfly.

Super-hydrophobic characteristics of butterfly wing surface

Many biological surface are hydrophobic because of their complicated composition and surface microstructure. Eleven species (four families) of butterflies were selected to study their micro-, nano-structure and super-hydrophobic characteristic by means of Confocal Light Microscopy, Scanning Electron Microscopy and Contact Angle Measurement. The contact an- gles of water droplets on the butterfly wing surface were consistently measured to be about 150 ? and 100 ? with and without the squamas, respectively. The dust on the surface can be easily cleaned by moving spherical droplets when the inclining angle is larger than 3 ?. It can be concluded that the butterfly wing surface possess a super-hydrophobic, water-repellent, self-cleaning, or “Lotus-effect”characteristic. The contact angle measurement of water droplets on the wing surface with and without the squamas showed that the water-repellent characteristic is a consequence of the microstructure of the squamas. Each water droplet (diameter 2 mm) can cover about 700 squamas with a size of 40 m×80 m of each squama. The regular riblets with a width of 1000 nm to 1500 nm are clearly observed on each single squama. Such nanostructure should play a very important role in their super-hydrophobic and self-cleaning characteristic.

Study on Five Methods for Extracting DNA from Dried Butterfly Specimen

[Objective]The aim was to find out the optimal methods for extracting DNA from dried butterfly specimen.[Method]A total of five methods including SDS method,SDS-mercaptoethanol-phenol method,CTAB method,saturated NaCl method,SDS-mercaptoethanol-chloroform methods were employed to extract DNA from dried butterfly specimen.[Result]SDS-mercaptoethanol-phenol method,saturated NaCl method and SDS-mercaptoethanol-chloroform method were suitable for genomic DNA extraction from dried butterfly samples,and the DNA extracted can be successfully applied to the PCR amplification of butterfly mitochondrial COI,EF-1α and CytB genes.[Conclusion]SDS-mercaptoethanol-phenol method,saturated NaCl method and SDS-mercaptoethanol-chloroform method can be used to study the extraction of DNA from dried butterfly samples,which laid foundation for study on molecular phylogenetics of butterfly.

Effects of Methanol on Wettability of the Non-Smooth Surface on Butterfly Wing

The contact angles of distilled water and methanol solution on the wings of butterflies were determined by a visual contact angle measuring system. The scale structures of the wings were observed using scanning electron microscopy, The influence of the scale micro- and ultra-structure on the wettability was investigated. Results show that the contact angle of distilled water on the wing surfaces varies from 134.0° to 159.2°. High hydrophobicity is found in six species with contact angles greater than 150°. The wing surfaces of some species are not only hydrophobic but also resist the wetting by methanol solution with 55% concentration. Only two species in Parnassius can not resist the wetting because the micro-structure （spindle-like shape） and ultra-structure （pinnule-like shape） of the wing scales are remarkably different from that of other species. The concentration of methanol solution for the occurrence of spreading/wetting on the wing surfaces of different species varies from 70% to 95%. After wetting by methanol solution for 10 min, the distilled water contact angle on the wing surface increases by 0.8°-2.1°, showing the promotion of capacity against wetting by distilled water.