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.

Elevational distribution of butterflies in the Himalayas: a case study from Langtang National Park, Nepal

Mountain ecosystems are relatively more vulnerable to climate change since human induced climate change is projected to be higher at high altitudes and latitudes. Climate change induced effects related to glacial response and water hazards have been documented in the Himalayas in recent years, yet studies regarding species' response to climate change are largely lacking from the mountains and Himalayas of Nepal. Changes in distribution and latitudinal/altitudinal range shift, which are primary adaptive responses to climate change in many species,are largely unknown due to unavailability of adequate data from the past. In this study, we explored the elevational distribution of butterflies in Langtang Village Development Committee(VDC) of Langtang National park; a park located in the high altitudes of Nepal. We found a decreasing species richness pattern along the elevational gradient considered here.Interestingly, elevation did not appear to have a significant effect on the altitudinal distribution ofbutterflies at family level. Also, distribution of butterflies in the area was independent of habitat type,at family level. Besides, we employed indicator group analysis(at family level) and noticed that butterfly families Papilionidae, Riodinidae, and Nymphalidae are significantly associated to high, medium and low elevational zone making them indicator butterfly family for those elevational zones, respectively. We expect that this study could serve as a baseline information for future studies regarding climate change effects and range shifts and provide avenues for further exploration of butterflies in the high altitudes of Nepal.

The type specimens of European butterflies from the Linnaeus collection(Lepidoptera:Rhopalocera)

The status of primary types of European butterflies established by C.Linnaeus is revised.Lectotype status is confirmed for 38 taxa.Lectotypes of the following taxa are designated in this paper:Papilio apollo Linnaeus,1758;P.daplidice Linnaeus,1758 and P.palaeno Linnaeus,1761.For the following 18 species-group taxa,the status of primary types changed from lectotypes to holotypes(by monotypy)due to their presence in the Linnean collection by single specimens:Papilio aglaja Linnaeus,1758;P.atalanta Linnaeus,1758;P.boeticus Linnaeus,1767;P.cardui Linnaeus,1758;P.cinxia Linnaeus,1758;P.deianira Linnaeus,1764;P.euphrosyne Linnaeus,1758;P.hero Linnaeus,1761;P.janira Linnaeus,1758;P.jurtina Linnaeus,1758;P.lathonia Linnaeus,1758;P.levana Linnaeus,1758;P.megera Linnaeus,1767;P.paphia Linnaeus,1758;P.polychloros Linnaeus,1758;P.rhamni Linnaeus,1758;P.rubi Linnaeus,1758 and P.sinapis Linnaeus,1758.Butterflies that do not have a pronounced sexual dimorphism and bright and contrasting coloration in the Linnean collection are represented in most cases by a single type specimen.The largest,brightest and most beautiful butterflies in the Linnean collection have a type series of the maximum size(4 specimens).There are no series of 5 or more specimens for European butterflies in Linnaeus’collection.

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.

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.

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。

The movement of ＂false antennae＂ in butterflies with ＂false head＂ wing patterns

In many butterfly species of the family Lycaenidae, the morphology and color pattern of the hind wings, together with certain behaviors, suggests the presence of a false head （FH） at the posterior end of the perching individual. This FH is consi- dered an adaptation to escape from visually oriented predators. A frequent component of the FH are the tails that presumably resemble the antennae, and the typical hind wings back-and-forth movement along the sagittal plane （HWM） performed while perching apparently move the tails in a way that mimics antennal movement. By exposing 33 individuals from 18 species of Lycaenidae to a stuffed insectivorous bird, we tested two alternative hypotheses regarding HWM. The first hypothesis proposes that, when the butterfly is observed at close range, the HWM distorts the shape of the false head thus reducing its deceiving effect and, therefore, selection will favor butterflies that stop moving their wings when a predator is close by; the second hypothesis says that an increase in the frequency of HWM improves its deflective effect when the butterfly confronts a predator at close range. Our results tend to support the second hypothesis because half of the butterflies started to move their hind wings or increased the rate of HWM when exposed to the stuffed bird; however a substantial proportion of butterflies （30%） stopped moving their hind wings or decreased the rate of HWM as expected from the first hypothesis. Our observations also showed that there is great variation in the rates of HWM, and demonstrated the existence of alternative ways of producing ＂vivid＂ movement of the hind wing tails （the ＂false antennae＂） in the absence of HWM [Current Zoology 61 （4）： 758-764, 2015].

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.