Impacts of Human Activities and Season on Species Diversity and Abundance of Butterflies in Mpanga Kipengere Game Reserve and Surrounding Farmlands, Tanzania

Maintaining natural habitats is crucial for the preservation of insects and other species that indicate environmental changes. However, the Mpanga/Kipengere Game Reserve and its surrounding farmlands are facing disturbance due to human activities, which is putting many wildlife species, particularly larger mammals, at risk. To determine the impact of human activities on butterfly species diversity and abundance in the reserve and its surrounding areas, we conducted a study from November 2021 to October 2023. We collected butterfly data using transect walks and baited traps in two habitat types. Our study yielded 2799 butterfly Individuals ranging in 124 species divided into five families habitat, season, and anthropogenic factors are significant environmental variables influencing species diversity and abundance of butterflies. Therefore, it’s important to protect habitat and dry-season water for the conservation of invertebrates such as butterflies. Our study findings provide essential information for ecological monitoring and future assessment of the Mpanga/Kipengere Game Reserve ecosystem health.

Butterflies Extracts Show Antibacterial Activity

Extracts of several British butterfly species were tested and shown to possess powerful bactericidal activity against gram-positive bacteria (tested on Staphylococcus aureus and Bacillus anthracis). The active compounds in the grass-feeding species were identified as hydroxylated pyrrolizidine alkaloids (PAs) related to loline with nitrogen at C-1. Lolines are known insecticidal and insect-deterrent compounds that are produced in grasses infected by endophytic fungal symbionts. Lolines also increase resistance of endophyte-infected grasses to insect herbivores. The butterfly-isolated pyrrolizidine alkaloids appear to be novel and non-toxic to human cells such as HaCat human skin keratinocytes and Hep-2 human epithelial cells. The discovery of novel agents from butterflies could lead to the development of new antimicrobials.

High-quality reference genomes of swallowtail butterflies provide insights into their coloration evolution

Swallowtail butterflies(Papilionidae)are a historically significant butterfly group due to their colorful wing patterns,extensive morphological diversity,and phylogenetically important position as a sister group to all other butterflies and have been widely studied regarding ecological adaption,phylogeny,genetics,and evolution.Notably,they contain a unique class of pigments,i.e.,papiliochromes,which contribute to their color diversity and various biological functions such as predator avoidance and mate preference.To date,however,the genomic and genetic basis of their color diversity and papiliochrome origin in a phylogenetic and evolutionary context remain largely unknown.Here,we obtained high-quality reference genomes of 11 swallowtail butterfly species covering all tribes of Papilioninae and Parnassiinae using long-read sequencing technology.Combined with previously published butterfly genomes,we obtained robust phylogenetic relationships among tribes,overcoming the challenges of incomplete lineage sorting(ILS)and gene flow.Comprehensive genomic analyses indicated that the evolution of Papilionidae-specific conserved non-exonic elements(PSCNEs)and transcription factor binding sites(TFBSs)of patterning and transporter/cofactor genes,together with the rapid evolution of transporters/cofactors,likely promoted the origin and evolution of papiliochromes.These findings not only provide novel insights into the genomic basis of color diversity,especially papiliochrome origin in swallowtail butterflies,but also provide important data resources for exploring the evolution,ecology,and conservation of butterflies.

法国首座利用生物乙醇生产丁二烯的工业示范装置落成

Michelin、IFPEN和Axens为法国第一座生物基丁二烯生产工业示范项目共同揭幕,该项目位于法国波尔多附近的巴桑米其林工厂,在BioButterfly项目框架内由3个合作伙伴共同建设,并得到了法国环境和能源管理局(ADEME)的支持,旨在开发和商业化来自生物质(植物)的乙醇生产的丁二烯,以替代石化产品中的丁二烯。BioButterfly项目在创建生物基合成弹性体产业方面迈出了重要一步。迄今为止,BioButterfly项目总投资超过8000万欧元,其中1470万欧元来自ADEME“未来投资计划”的支持。

A design scheme for interference suppression based on CWBIM

An interference suppression design scheme based on conjugate weighted butterfly interleaving mapping(CWBIM)is proposed for inter-carrier interference(ICI)and inter-subband interference(IBI)in the received signals of universal filtered multi-carrier(UFMC)systems.It applies an interleaving mapping operation to subtract the interference coefficients of adjacent terms in ICI and IBI twice,thereby achieving suppression effects similar to the self-cancellation(SC)algorithm while maintaining the original data transmission efficiency.Meanwhile,conjugate and complex weighting operations can effectively suppress the impact of phase rotation errors in high-speed mobile channel environments,thereby further improving the bit error rate(BFR)performance of the system,Moreover,butterfly operation can effectively control the computational complexity of the interleaving mapping process.Theoretical analysis and simulation results show that,compared with the PSC-UFMC algorithm,the CWBIM-UFMC scheme proposed in this paper can effectively suppress ICI and IBI in the received signal without compromising data transmission efficiency and reducing computational complexity,thereby achieving good BER performance of the system.

米其林在法启用首个工业级生物基丁二烯生产示范工厂

近日,米其林携手法国石油研究院(IFPEN)和艾克森斯公司(Axens),在法国巴森正式启用首个工业级生物基丁二烯生产示范工厂。该示范工厂在BioButterfly项目框架内成立,并得到法国环境与能源管理署(ADEME)大力支持,旨在利用生物乙醇生产丁二烯并将其商业化,以替代从化石燃料中提取的丁二烯。丁二烯是一种用于聚合物制造的有机化合物,被广泛应用于清漆、树脂、ABS塑料、汽车用尼龙、纺织品和建筑等领域,而目前全球约40%的丁二烯被用于轮胎制造。

Butterflies Flap into A Pond

Ingredients: 150 grams grass carp, 50 grams beef tripe, 15 grams of various kinds of mushrooms, 5 grams of Chinese wolfberry, 5 grams coriander, salt and pepper to taste. Optional: MSG.

Presence of a cerebral factor showing summer-morph- producing hormone activity in the brain of the seasonal non- polyphenic butterflies Vanessa cardui, V. indica and Nymphalis xanthomelas japonica （Lepidoptera： Nymphalidae）

Three species ofnymphalid butterflies, Vanessa cardui, V. indica and Nymphalis xanthomelasjaponica, do not exhibit seasonal polyphenism in wing coloration. To deter-mine whether seasonal non-polyphenic butterflies possess a cerebral factor affecting wing coloration, we used a Polygonia c-aureurn female short-day pupal assay for detection of summer-morph-producing hormone （SMPH） activity in P. c-aureum. When 2% NaCl extracts of 25 brain-equivalents prepared from the pupal brains of V. cardui, V. indica or N. xanthomelasjaponica were injected into Polygonia female short-day pupae, all recipients developed into summer-morph adults with dark-yellow wings, and the average grade score （AGS） of summer morphs showing SMPH activity was 3.8, 3.7 and 4.0, respectively. In contrast, when acetone or 80% ethanol extracts prepared from pupal brains were injected into Polygonia pupae, all recipients developed into autumn-morph adults with a dark-brown coloration and each exhibited an AGS of less than 0.5. Our results indicate that a cerebral factor showing SMPH activity is present in the pupal brain of seasonal non-polyphenic nymphalid butterflies, suggesting that a SMPH and cerebral factor showing SMPH activity occur widely among butterfly species. This finding will improve our understanding of the presence of cerebral factors showing interspecific actions of SHPH.

The Unique Strategies of Flight Initiation Adopted by Butterflies on Vertical Surfaces

As the basis of flight behavior,the initiation process of insect flight is accompanied by a transition from crawling mode to flight mode,and is clearly important and complex.Insects take flight from a vertical surface,which is more difficult than takeoff from a horizontal plane,but greatly expands the space of activity and provides us with an excellent bionic model.In this study,the entire process of a butterfly alighting from a vertical surface was captured by a high-speed camera system,and the movements of its body and wings were accurately measured for the first time.After analyzing the movement of the center of mass,it was found that before initiation,the acceleration perpendicular to the wall was much greater than the acceleration parallel to the wall,reflecting the positive effects of the legs during the initiation process.However,the angular velocity of the body showed that this process was unstable,and was further destabilized as the flight speed increased.Comparing the angles between the body and the vertical direction before and after leaving the wall,a significant change in body posture was found,evidencing the action of aerodynamic forces on the body.The movement of the wings was further analyzed to obtain the laws of the three Euler angles,thus revealing the locomotory mechanism of the butterfly taking off from the vertical surface.

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.

On Some Ev-Degree and Ve-Degree Dependent Indices of Benes Network and Its Derived Classes

One of the most recent developments in the field of graph theory is the analysis of networks such as Butterfly networks,Benes networks,Interconnection networks,and David-derived networks using graph theoretic parameters.The topological indices(TIs)have been widely used as graph invariants among various graph theoretic tools.Quantitative structure activity relationships(QSAR)and quantitative structure property relationships(QSPR)need the use of TIs.Different structure-based parameters,such as the degree and distance of vertices in graphs,contribute to the determination of the values of TIs.Among other recently introduced novelties,the classes of ev-degree and ve-degree dependent TIs have been extensively explored for various graph families.The current research focuses on the development of formulae for different ev-degree and ve-degree dependent TIs for s−dimensional Benes network and certain networks derived from it.In the end,a comparison between the values of the TIs for these networks has been presented through graphical tools.

A Glimpse into the Natural History of Leiolepis rubritaeniata Mertens,1961(Squamata:Leiolepididae)

During the months of April through July 2020 we studied aspects of the natural history of Leiolepis rubritaeniata,a species of butterfly lizard that occurs on the Khorat Plateau in Thailand and adjacent regions of Laos and Cambodia as well as in south-central Vietnam.We present data on population size and structure,as well as location,size,compass orientation,and structure of the lizard’s burrows.Also,we present climatic data(ambient air temperature and precipitation)at the study site for the duration of our field work as well as temperature data for the complete year 2021.Furthermore,we provide data on egg laying,incubation conditions and characteristics of the hatchlings.Finally,we present an easy to use and reliable non-invasive method for the long-term recognition of individual butterfly lizards based on their unique dorsal patterning.Butterfly lizards are utilized as a food source for the local human population.So far,there are no farming projects in Thailand involving this species and all individuals are collected from the wild populations.No data are available on the population dynamics of L.rubritaeniata but it can be assumed that habitat destruction due to land use change as well as its utilization for human consumption have negative effects on the long-term survival of the local populations of this lizard species.The baseline data presented here are essential for any meaningful conservation strategy for these lizards.

Fruit Leaf Diseases Classification: A Hierarchical Deep Learning Framework

Manual inspection of fruit diseases is a time-consuming and costly because it is based on naked-eye observation.The authors present computer vision techniques for detecting and classifying fruit leaf diseases.Examples of computer vision techniques are preprocessing original images for visualization of infected regions,feature extraction from raw or segmented images,feature fusion,feature selection,and classification.The following are the major challenges identified by researchers in the literature:(i)lowcontrast infected regions extract irrelevant and redundant information,which misleads classification accuracy;(ii)irrelevant and redundant information may increase computational time and reduce the designed model’s accuracy.This paper proposed a framework for fruit leaf disease classification based on deep hierarchical learning and best feature selection.In the proposed framework,contrast is first improved using a hybrid approach,and then data augmentation is used to solve the problem of an imbalanced dataset.The next step is to use a pre-trained deep model named Darknet53 and fine-tune it.Next,deep transfer learning-based training is carried out,and features are extracted using an activation function on the average pooling layer.Finally,an improved butterfly optimization algorithm is proposed,which selects the best features for classification using machine learning classifiers.The experiment was carried out on augmented and original fruit datasets,yielding a maximum accuracy of 99.6%for apple diseases,99.6%for grapes,99.9%for peach diseases,and 100%for cherry diseases.The overall average achieved accuracy is 99.7%,higher than previous techniques.

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.

Effective Customer Review Analysis Using Combined Capsule Networks with Matrix Factorization Filtering

Nowadays,commercial transactions and customer reviews are part of human life and various business applications.The technologies create a great impact on online user reviews and activities,affecting the business process.Customer reviews and ratings are more helpful to the new customer to purchase the product,but the fake reviews completely affect the business.The traditional systems consume maximum time and create complexity while analyzing a large volume of customer information.Therefore,in this work optimized recommendation system is developed for analyzing customer reviews with minimum complexity.Here,Amazon Product Kaggle dataset information is utilized for investigating the customer review.The collected information is analyzed and processed by batch normalized capsule networks(NCN).The network explores the user reviews according to product details,time,price purchasing factors,etc.,ensuring product quality and ratings.Then effective recommendation system is developed using a butterfly optimized matrix factorizationfiltering approach.Then the system’s efficiency is evaluated using the Rand Index,Dunn index,accuracy,and error rate.

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.

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.

新型可重构移位-置换单元研究与设计

本文利用Inverse Butterfly/Butterfly多级动态网络的自重构特性,提出了一种针对循环移位操作的高速可重构控制信息生成算法,该算法不仅具有极高的并行性和较小的资源消耗,还首次将循环移位、双向循环移位和以2~i(i=0,1,2…)为位宽的短字循环移位等10余种不同类型的移位操作统一在一个算法中.并在此基础上,设计了一种新型可重构移位-置换单元.该单元在SMIC 65nm工艺完成了逻辑综合.实验结果表明,当该单元只实现循环移位时,与以往研究成果相比,频率提升了6.4%~12%,面积减小了22%~30%;当该单元实现多种移位操作时,频率下降约8.4%,但能够支持的移位操作类型是以往研究成果的2倍.

不同营养液配方对水培白蝴蝶合果芋的影响

为探究适宜白蝴蝶合果芋(Syngonium podophyllum cv.White Butterfly)水培生长的营养液大中量元素配方,试验选取5种常见植物水培营养液配方以及1种自行调制配方,以清水为对照,观察记录7种处理下白蝴蝶合果芋生长状况及其水培溶液变化,进而筛选出适宜白蝴蝶合果芋水培使用的营养液大中量元素配方比例。结果表明,水培能有效促进白蝴蝶合果芋的生长。营养液的添加对白蝴蝶合果芋的根长伸长与株高生长作用并不明显,但不同营养液配方比例下白蝴蝶合果芋在其他生长指标上差异显著。其中,自行调制的营养液配方能很好地促发侧根生长,促进叶片的生长及冠幅的增大;山崎三叶芹配方能有效促进植株不定根的抽生和冠幅的增大;华南农大叶菜B配方、山崎三叶芹配方以及自行调制配方均能维持并促进新叶展开速率;而改良Hoagland配方、日本园试配方以及观叶植物通用配方因为对植株根系与展叶的生长不理想,不太适合用于白蝴蝶合果芋的水培培养。尽管这些营养液能促进白蝴蝶合果芋水培生长,但其使用时的pH与电导率还有待后期进一步优化与调整,以维持溶液的稳定性。

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。