Search

In medical institutions, radiation shielding is an effective strategy to protect medical personnel and patients from exposure. Reducing the weight of the shield worn by medical personnel in the radiation generating area plays a key role in improving their productivity and mobility. In this study, a new lightweight radiation shield was developed by electrospinning a polymer-tungsten composite material to produce nanofibers with a multi-layered thin-film structure similar to that of a morpho butterfly wing. The fabricated shield was in the form of 0.1 mm thick flexible shielding paper. The multi-layer structure of the thin shielding paper was obtained through nanofiber pattern formation via electrospinning a dispersion of tungsten particles. At 0.1 mm thickness, the paper's shielding rate was 64.88% at 60 keV. Furthermore, at 0.3 mm thick and arranged in a laminated structure, the shielding rate was 90.10% and the lead equivalent was 0.296 mmPb. When used as an apron material, the weight can be reduced by 45% compared to existing lead products. In addition, the material is highly processable and can be used to manufacture various flexible products, such as hats, gloves, underwear, and scarves used in medical institutions. [ABSTRACT FROM AUTHOR]

The consequences of the partially coerced opening of Japan to the Western world in the second half of the 19th century went far beyond economic and political goals and considerations. The previously secluded land almost instantly became a source of artistic inspiration and endless fascination. Japonisme, the term by which the latest craze become known in France, was no passing fad. For many decades, Western artists, most of whom had never set foot in Japan, derived profound inspiration from all facets of the mysterious culture which unfolded in the period. Thus, with scant information and a lack of accurate records being available, common gossip and unfounded rumor filled in the blanks of official reports and naval tales, connecting the dots between the real and the imagined. In this paper, I succinctly examine the story of Madame Butterfly, cutting across time, genre and borders in the works of John Luther Long, David Belasco, Giacomo Puccini and Claude-Michel Schönberg/Alain Boublil. I contextualize the selected narratives within their socio-political frameworks, but also consider the ramifications of the past and presentday adaptations from the 21st-century perspective, in the light of current struggles for (adequate) representation. Lastly, I examine the production of Miss Saigon (2019-22) at the Music Theatre of Łódź, Poland to compare how the staging of such a musical in a predominantly racially homogenous country affects the perception of Orientalist works. As such this section is a case study based on personal interviews conducted by the author with the producers and cast members. [ABSTRACT FROM AUTHOR]

Due to the multimodal nature of real-world optimization problems, in recent years, there has been a great interest in multi-modal optimization algorithms. Multimodal optimization problems involve identifying multiple local/global optima. Niching techniques have been widely used to tackle multi-modal optimization problems. Most of the existing niching methods either require predefined niching parameters or extra information about the problem space. This paper presents a novel multimodal algorithm based on Butterfly Optimization Algorithm, which is constructed using the Fitness-Distance Balance (FDB) selection method. The purpose of applying the FDB selection method is to discover local/global optima with high potential as a fitness value along with the appropriate distance from solution candidates. Also, a local search scheme is used to enhance the convergence speed of the algorithm. Niching is a technique used in multimodal optimization to maintain diversity among multiple solutions in the population. The minimum distance between the solutions is called a "niche". The proper niching radius is the main challenge for existing approaches. Knowing the problem space helps determine the niche radius. This paper proposes a new multimodal optimization scheme that does not require prior knowledge of the problem space or the niching parameter. Seven state-of-the-art multi-modal optimization algorithms are compared to the multi-modal butterfly optimization algorithm (MBOA) on 16 benchmarks from the CEC 2013 and CEC 2015 competitions to evaluate its performance. Success rate, Number of function evaluations, Success performance, average number of optima found, Success accuracy, Maximum peak ratio, and Run-time performance criteria were measured over 25 runs to assess the efficiency of the proposed method. The experimental results demonstrate that MBOA outperforms other algorithms according to most of the performance criteria. [ABSTRACT FROM AUTHOR]

In this paper, we take a historical perspective by going back to Verschaffelt's landmark study published in 1910, in which he found that glucosinolates were used as token stimuli by larvae of Pieris butterflies, specialist feeders on plants in the family Brassicaceae. This classic discovery provided key evidence for Fraenkel (Science 129:1466–1470, 1959) to elaborate on the function of secondary plant substances and for Ehrlich and Raven (Evolution 18:586–608, 1964) to put forward the hypothesis of insect–plant coevolution. The discovery by Schoonhoven (Kon Nederl Akad Wetensch Amsterdam Proc Ser C70:556–568, 1967) of taste neurons highly sensitive to glucosinolates in Pieris brassicae was an important milestone in elucidating the chemosensory basis of host-plant specialization. The molecular basis of glucosinolate sensitivity was elucidated recently (Yang et al., PLoS Genet 17, 2021) paving the way to unravel the evolution of gustatory receptors tuned to glucosinolates that are crucial for host-plant selection of Pieris butterflies. We propose a hypothetical model for the evolution of labeled-line neurons tuned to token stimuli. [ABSTRACT FROM AUTHOR]

Optical nanoantennas demonstrate the ability to confine and enhance electromagnetic fields. This ability makes nanoantennas essential tools for highresolution microscopy. The nanoantenna resonance and response can be tuned by changing their size, shape, and material as well as adjusting the probing conditions, e.g. excitation wavelength. In this paper we simulated the propagation and interaction of visible light with computer generated models of butterfly nanoantenna arrays using the finite-difference time-domain (FDTD) method. The simulations were used to understand and predict the experimental results obtained with scanning nearfield microscopy (SNOM) on commercially available samples. Simulation parameters are chosen carefully to reflect the measurement conditions. [ABSTRACT FROM AUTHOR]

Lorenz rediscovered the butterfly effect, which is defined as the sensitive dependence on initial conditions (SDIC), in 1963. In 1972, he used the term "butterfly" as a metaphor to illustrate how a small perturbation can lead to a tornado with a complex structure. The metaphorical butterfly effect, which celebrated its 50th anniversary in 2022, is not precisely the same as the original butterfly effect with SDIC. To commemorate the 50th anniversary, a Special Issue was launched and invited the submission of research and review articles that can help to enhance our understanding of both the original and metaphorical butterfly effects. The Special Issue also sought recent developments in idealized Lorenz models and real-world models that address multistability, multiscale predictability, and sensitivity. The call for papers was opened 15 months prior to the completion of the Special Issue and features nine selected papers. This editorial provides a brief review of Lorenz models, introduces the published papers, and summarizes each one of them. [ABSTRACT FROM AUTHOR]

In 2017, David Henry Hwang reworked his Tony Award‐winning play M. Butterfly (1988). This paper explores how Hwang wrote and rewrote M. Butterfly from the perspective of ethical literary criticism, probing into the role of brain text played in the process of drama writing. By investigating the variations of the Madame Butterfly story—from love legend to spy story—and the Madame Butterfly image—from Madame Butterfly to M. Butterfly, from Oriental Butterfly to Chinese Butterfly—this paper explains the forming of Hwang's brain text and his creation of M. Butterfly, the decoding of brain text and his writing of two Monsieur Butterflies, as well as the recoding of brain text and his building of a new cultural identity. It argues that brain text determines the playwright's writings and styles, and the dynamic revisions of M. Butterfly can be the manifestation and reconstruction of Hwang's brain texts, which represent his ethical choices made in the process of writing and rewriting. The metamorphosis of "Butterfly" in the two scripts of M. Butterfly reveals the playwright's dynamic thoughts on culture, gender, and identity: cultural stereotypes and the power relation between East and West; the cultural misunderstanding of Orientalism; and gender identity and fluidity in the twenty‐first century. [ABSTRACT FROM AUTHOR]

For endangered species with limited dispersal capacity, the preservation of individual patches or sites is extremely important. The quality and size of habitat patches and their isolation from other patches are the main parameters for an assessment of population persistence, but translating persistence probabilities into practical measures is still a weak link in conservation management. I provide a quantitative conservation approach for the endangered myrmecophilous butterfly Maculinea alcon in the Netherlands. All 127 colonies known on heathland since 1990 were investigated for patch quality, size, and isolation. I assessed habitat quality in three 10 × 10 m plots for most colonies. Site occupancy in 1998–1999 was only 56%. Occupancy was best explained by a logistic regression including patch area, host ant presence, host plant abundance, overall heathland area, and connectivity between sites ( 2= 0.410, < 0.0001); it correctly classified the occupied or vacant status for 82% of the sites. Connectivity contributed only 3.6% to the total explained variation of site occupancy, indicating that habitat characteristics were more important than isolation in determining population persistence at the examined scale level (>500 m). Grazing and sod cutting had a beneficial impact, but in combination these practices proved detrimental. Hydrological measures to prevent drainage were also associated with lowered occupancy. I used the different components in the logistic regression to formulate objective management recommendations. These consisted of sod cutting, reduction of management intensity, enlargement of habitat, or combinations of these recommendations. The results highlight the importance of careful management when site quality is determined by multiple factors. The quantitative conservation approach followed here can be fruitfully extended to other endangered species, provided enough is known about their ecological requirements and how management actions affect them. [ABSTRACT FROM AUTHOR]

Throughout South-East Asia, droughts associated with ENSO (El Niño Southern Oscillation) events have resulted in large-scale fires affecting millions of hectares of rain forest. However, the long-term impacts of these fires on the rain forest faunas are only poorly understood. Our aim was to study the recovery of rain forest butterfly assemblages following the 1997–98 ENSO event, which resulted in the largest-scale fires in the recorded history of the region. A 420-km2 area in the Balikpapan-Samarinda region of East Kalimantan, Indonesian Borneo. Four landscapes were assessed after the 1997–98 ENSO event, including one landscape that was assessed prior to the event. Comparisons of species richness, species composition and guild abundance were made among landscapes and years. The relative importance of environment, geographical distance between sampling sites, and time between sampling years was quantified during the succession phase using a variance partitioning technique. The fires dramatically altered the butterfly community and resulted in a major decline in observed species richness within the landscape surveyed prior to the ENSO event. Following fires in 1998, butterfly assemblages in all landscapes were dominated by large-winged generalist species. During 1999 and 2000, assemblages became increasingly dominated by smaller specialist species. Species endemic to Borneo that were present before fires were absent in 2000, despite intensive sampling over enhanced spatial and environmental scales. Community similarity was significantly dependent upon local environmental variables, geographical distance between sampling sites, and time between sampling years. Together, these explained over 52% of the observed variation in samples. The importance of geographical distance between sampling sites indicates that recovery was dependent upon colonization from proximate habitats. Despite an apparent trend of return to pre-ENSO community structure, low species richness throughout the survey area indicates that full recovery had not taken place by 2000. [ABSTRACT FROM AUTHOR]

Path planning is one of the most critical issues in many related fields including UAVs. Many researchers have addressed this problem according to different conditions and limitations, but modelling the 3-D space and routing with an evolutional algorithm in such spaces is an open issue. So, in this paper, we first, introduce a method to grids the environment using geometrical shapes. This can reduce the random states of cell decomposition and increases the computational speed. We then propose an effective routing algorithm based on the butterfly optimization algorithm (BOA). It can simultaneously optimize multiple path planning objectives. It uses an objective function to compute the shortest path, based on obstacle avoidance and the UAV's operational power minimization. A novel concept, the intelligent throwing agent, used in this algorithm prevents getting stuck in local optima and increases the network coverage in path planning. The throwing agent prevents the collision of the UAV with the obstacles using geometrical techniques and contour lines. The simulation results show that BOA has the least and second-least cost in best-case and worst-case scenarios in comparison with ant colony and particle swarm. Its run time and the optimal value of the fitting function are also better than the two other algorithms. [ABSTRACT FROM AUTHOR]

As small poikilotherms, insects are largely dependent on their environment for thermoregulation and so are particularly vulnerable to changing temperatures.Butterflies are a well‐studied group often used as models to investigate insect responses to temperature. However, little has been done to synthesise and present this large volume of literature in an accessible format, particularly with reference to knowledge gaps and areas rich in information. Using a systematic mapping method, we synthesised the last 40 years of research on the topic of butterfly responses to temperature.We identified and coded 451 research papers, in which butterfly species were studied 3198 times. We identified taxonomic groups, regions and experimental designs that were well or poorly represented.We found that there was a relatively good balance of representation across butterfly families in relation to the number of species within each family. The tropics were less frequently studied than temperate regions, and there were more studies reporting outcomes on adults than at any other life stage. Finally, in situ studies were more common than ex situ studies.Taken together, the higher representation of certain regions, life stages and approaches could lead to an incomplete understanding of the impacts of temperature on butterflies, potentially resulting in ill‐informed decisions.We make suggestions for how to resolve these discrepancies in representation, including calling for an increased focus on the tropics, the establishment of butterfly monitoring schemes in the global south, a greater focus on the effects of temperature on non‐adult life stages, an increase in experiments investigating fluctuating thermal regimes and the incorporation of more behavioural responses to temperature in future research. Only by addressing these disparities can we gain a complete understanding of how butterflies will respond to climate change. [ABSTRACT FROM AUTHOR]

The primary goal of this paper is to design a Multi-Converter Unified Power Quality Conditioner (MC-UPQC). The proposed method is solved based on the Synchronous Reference Frame (SRF) theory. The MC-UPQC involves two series Voltage Source Converters (VSCs) for power transfer between feeders to eliminate voltage sag, swell, interruption, and transient response in the system. The proposed model adopts control strategies based on an optimized Fuzzy Logic Controller (FLC) in SRF, utilizing a hybrid metaheuristic algorithm called Beetle Swarm-based Butterfly Optimization Algorithm (BS-BOA), which combines Beetle Swarm Optimization (BSO) and Butterfly Optimization Algorithm (BOA) for membership limit optimization and control rule generation. The major benefit of the optimized FLC-based MC-UPQC is its quick behavior in minimizing Total Harmonic Distortion (THD) in source and load side voltages and currents. Simulation and MATLAB environment are used for the entire system implementation. The comparative analysis of the proposed controller for MC-UPQC is performed against conventional controllers to validate its effectiveness. Quantitative data related to the main research outcomes, such as THD of source voltage and current, and dynamic behavior of the system, are included. The main benefit of the study is the significant reduction in THD and improved dynamic performance of the system. [ABSTRACT FROM AUTHOR]

To solve the drawbacks of the Otsu image segmentation algorithm based on traditional butterfly optimization, such as slow convergence speed and poor segmentation accuracy, this paper proposes hybrid fractional-order butterfly optimization with the Otsu image segmentation algorithm. G-L-type fractional-order differentiation is combined with the algorithm's global search to improve the position-updating method, which enhances the algorithm's convergence speed and prevents it from falling into local optima. The sine-cosine algorithm is introduced in the local search step, and Caputo-type fractional-order differentiation is used to avoid the disadvantages of the sine-cosine algorithm and to improve the optimization accuracy of the algorithm. By dynamically converting the probability, the ratio of global search to local search is changed to attain high-efficiency and high-accuracy optimization. Based on the 2-D grayscale gradient distribution histogram, the trace of discrete matrices between classes is chosen as the fitness function, the best segmentation threshold is searched for, image segmentation is processed, and three categories of images are chosen to proceed with the segmentation test. The experimental results show that, compared with traditional butterfly optimization, the convergence rate of hybrid fractional-order butterfly optimization with the Otsu image segmentation algorithm is improved by about 73.38%; meanwhile, it has better segmentation accuracy than traditional butterfly optimization. [ABSTRACT FROM AUTHOR]

In this paper we present for the first-time behavioral observations of symbiotic interactions between butterfly caterpillars and ants in the Pantanal wetlands of west-central Brazil (Mato Grosso and Mato Grosso do Sul states). Four species were found: Aricoris propitia (Stichel, 1910), Nymphidium olinda H. Bates, 1865, Synargis calyce (C. Felder & R. Felder, 1862) (Riodinidae), and Strymon mulucha (Hewitson, 1867) (Lycaenidae). In the Pantanal, these widely distributed species live in association with host plants and tending ants adapted to flooding. Except for A. propitia, which has an obligate association with Solenopsis saevissima (Smith, 1855) complex ants, the other butterfly species are facultatively myrmecophilous. A possible local adaptation was observed in A. propitia caterpillars, which pupated on the host plant instead of underground. Adaptations to flooding observed in plants, ants, and caterpillars, in addition to the existence of endemic species such as Synargis rasqueada Dolibaina, Dias, Mielke & Casagrande, 2013, suggest that wetland-specialized myrmecophiles may have evolved in the Pantanal. [ABSTRACT FROM AUTHOR]

This paper describes the flexible application of existential-humanistic (E-H) and relational approaches to supervision when working with adolescents with life-threatening illness. Powerful therapeutic work can occur through the balance of utilizing practical therapeutic interventions─including open-ended questions regarding existential anxieties, use of countertransference and somatic reactions, reflecting on values and priorities, dream interpretation, learning from each emotion, creativity, and what is psychological health─and understanding how an illness is processed through the patient's unique cultural, family, medical, and relational history. The relational approach provides a balance to the E-H therapy emphasis on individuality in its focus on the complexity and adhesiveness of how early relational patterns and expectations are repeated throughout life. The E-H approach, on the other hand, provides a richness and depth to adolescents' anxieties and desire for freedom and choice. The language of agency, uncertainty, groundlessness, and responsibility can be particularly relevant to the turbulence of this developmental stage. The ultimate goal of supervision is that the supervisor becomes part of the supervisee's "internal chorus" along with other teachers, therapists, and mentors when dealing with future clinical challenges. [ABSTRACT FROM AUTHOR]

The following paper outlines a study conducted on the abundance, distribution, and diversity of butterflies in Mt. Koritnik. located in the Republic of Kosovo, during 2019-2022. This research resulted in a total of 8166 recorded specimens, which belonged to 6 butterfly families, 50 genera and 131 species. The richest family in terms of abundance was Nymphalidae with 4611 specimens (56.47%), followed by Lycaenidae 1924 specimens (23.56%), Pieridae 856 (10.48%), 561 Hesperiidae (6.87%), Papilionidae 179 specimens (2.19%) and Riodinidae with 24 specimens (0.29%). In terms of species richness, Nymphalidae were the richest with 55 species, Lycaenidae 40, followed by 15 Pieridae, 15 Hesperiidae, 5 Papilionidae and 1 Roidinidae. Among 131 registered species, 11 have Near Threatened status in Europe. Our results indicated that species richness and abundance of butterflies were significantly negatively correlated with altitude (p<0.01), whereas they showed a strong positive correlation (p<0.01) with the temperature. The highest abundance and number of species were presented in the lower altitudinal range and the numbers decreased with altitude increasing. Activities such as intensive agriculture, grazing, fires and illegal timber cutting, which were observed during our survey, may be the main threats for butterflies in Mt. Koritnik in the future, therefore, we suggest the data from this research serve as a basic information for authorities to monitor future changes in butterfly diversity. [ABSTRACT FROM AUTHOR]

This paper studies a long butterfly strategy (which is immune to parallel curve shifts but exposed to non-parallel curve shifts) on the South African interest rate swap curve. Ten swap butterfly risk factors are identified using monthly South African swap data from 2001–2022. The top ranked butterfly strategies displayed strong and persistent outperformance over the bottom ranked strategies for each factor, resulting in improved risk-adjusted and absolute returns especially during positive and steep twists, and bull and bear flattening curve scenarios. Trade costs can be significant, so having a pragmatic rebalancing strategy with efficient market makers that limit trade costs to 1bps of spread make these swap butterfly risk factors an effective and successful portable alpha strategy. [ABSTRACT FROM AUTHOR]

The option butterfly portfolio is the commonly option arbitrage strategy. In reality, because the distribution of the option state price density (SPD) function is not normal and unknown, so the nonparametric deep learning methods to estimate option butterfly portfolio returns are proposed. This paper constructs the single-index nonparametric option pricing model which contains multiple influencing factors and presents the nonparametric estimation form for option butterfly portfolio returns. The empirical analysis shows that the SPD function estimated by using single-index nonparametric option model can effectively calculate the option butterfly portfolio returns with the minimum option strike price interval and provide an effective reference tool for risk-averse investors with limited risk preferences. [ABSTRACT FROM AUTHOR]

Foucard, Krishna and Lekshmi introduced a new graph-theoretic concept in the area of network monitoring. Let G be a graph with vertex set V (G) , there exists a subset S ⊆ V (G) , if deleting any edge in G, one can find that there exist at least two vertices in S whose distance is changed, then we call S is a monitoring edge-geodetic set (MEG-set for short). The minimum size of the MEG-set of G is called the monitoring edge-geodetic number of G (meg(G) for short). In this paper, we study the monitoring edge-geodetic number of some well-known networks, including Ladder, butterfly, circulant and Benes networks and convex polytopes. [ABSTRACT FROM AUTHOR]

This paper investigates four of the constitutional isomers of Si2H2+, namely the butterfly, vinylidene‐like, monobridged, and trans structures. These isomer geometries were all studied using the CCSD(T) method with basis sets as large as cc‐pV5Z. Higher level methods CCSDT and CCSDT(Q) were used for final energetics. It is found that the butterfly isomer has the lowest energy, followed by vinylidene‐like and monobridged structures; the trans structure has the highest energy of the four. All structures were compared with their neutral counterparts. Partial charges are computed and it is found that all isomers share the positive charge between the silicon atoms. NBO analysis shows that the cation Si−Si bond order is reduced by 0.5 relative to the neutral versions of the same isomer in the case of the butterfly, vinylidene‐like, and monobridged; and reduced by 0.6 for the trans isomer. Vibrational frequencies, infrared intensities, and dipole moments are predicted to encourage the spectroscopic identification of Si2H2+. [ABSTRACT FROM AUTHOR]

In Europe, 50%–70% of former natural grassland area has been destroyed during the past 30 years due to land use changes, losses are expected to increase in the future. Restoration is thought to reverse this situation by creating suitable abiotic conditions. In this paper, we investigate the effects of sod translocation with specific vegetation to facilitate the restoration of a former intensive agricultural field into a wet meadow. First, starting conditions were optimized including modification of the local hydrology, removal of the fertilized topsoil, application of liming, and translocation of fresh clippings as a seed source. The second part aimed at restoring the habitat for the butterfly species Phengaris (Maculinea) teleius, one of the species that was especially affected by the loss of wet meadows. This species engages in a complex myrmecophilous relationship with one host plant, Sanguisorba officinalis, and one obligate host ant, Myrmica scabrinodis. We used sod translocation to create islands of habitat to promote host plant and host ant colonization. After 4 years following the restoration, we observed that plants spread from the transplanted sods to the surroundings. The vegetation composition and structure of the transplanted sods attracted colonization of Myrmica ants into the restored areas. Following the increase in vegetation cover and height, Myrmica ant colonies further spread into the restored areas. Therefore, sod translocations can be considered an effective restoration method following topsoil removal in the process of restoring wet meadows to provide a starting point for ant colonization and plant dispersion. With these findings, this paper contributes to the evidence‐based restoration of wet meadows on former agricultural fields, including complex interactions between invertebrates and their required ecological relationships. [ABSTRACT FROM AUTHOR]

Supergenes are tightly linked sets of loci that are inherited together and control complex phenotypes. While classical supergenes—governing traits such as wing patterns in Heliconius butterflies or heterostyly in Primula—have been studied since the Modern Synthesis, we still understand very little about how they evolve and persist in nature. The genetic architecture of supergenes is a critical factor affecting their evolutionary fate, as it can change key parameters such as recombination rate and effective population size, potentially redirecting molecular evolution of the supergene in addition to the surrounding genomic region. To understand supergene evolution, we must link genomic architecture with evolutionary patterns and processes. This is now becoming possible with recent advances in sequencing technology and powerful forward computer simulations. The present theme issue brings together theoretical and empirical papers, as well as opinion and synthesis papers, which showcase the architectural diversity of supergenes and connect this to critical processes in supergene evolution, such as polymorphism maintenance and mutation accumulation. Here, we summarize those insights to highlight new ideas and methods that illuminate the path forward for the study of supergenes in nature. This article is part of the theme issue 'Genomic architecture of supergenes: causes and evolutionary consequences'. [ABSTRACT FROM AUTHOR]

This paper considers the problem of finding the position of a passive target using noisy time difference of arrival (TDOA) measurements, obtained from multiple transmitters and a single receiver. The maximum likelihood (ML) estimator's objective function is extremely nonlinear and non-convex, making it impossible to use traditional optimization techniques. In this regard, this paper proposes the chaos-enhanced adaptive hybrid butterfly particle swarm optimization algorithm, named CAHBPSO, as the hybridization of butterfly optimization (BOA) and particle swarm optimization (PSO) algorithms, to estimate passive target position. In the proposed algorithm, an adaptive strategy is employed to update the sensory fragrance of BOA algorithm, and chaos theory is incorporated into the inertia weight of PSO algorithm. Furthermore, an adaptive switch probability is employed to combine global and local search phases of BOA with the PSO algorithm. Additionally, the semidefinite programming is employed to convert the considered problem into a convex one. The statistical comparison on CEC2014 benchmark problems shows that the proposed algorithm provides a better performance compared to well-known algorithms. The CAHBPSO method surpasses the BOA, PSO and semidefinite programming (SDP) algorithms for a broad spectrum of noise, according to simulation findings, and achieves the Cramer–Rao lower bound (CRLB). [ABSTRACT FROM AUTHOR]

This paper presents a checklist of the butterfly fauna from the Apuseni Natural Park. The study aimed to inventory the fauna of Lepidoptera and their distribution in the park. In the investigated area, a number of 56 species of Lepidoptera were recorded. The research study of butterfly species in the southeastern area of the Apuseni Natural Park was realized over two years (2021 and 2022). In the researched area, the following species protected by European and/or National Legislation were identified: Parnassius mnemosyne, Neptis sappho, Euphydryas maturna and Euplagia quadripunctaria. Threats to some populations of butterflies were reported, predominantly the loss of preferential habitats. Several measures have been proposed for the conservation of the Lepidoptera species including maintaining preferential habitats, mowing meadows at the right time and moderate grazing of animals. [ABSTRACT FROM AUTHOR]

Convergence analysis of any random search algorithm helps to verify whether and how quickly the algorithm guarantees convergence to the point of interest. Butterfly optimization algorithm (BOA) is a popular population-based stochastic optimizer introduced by mimicking the foraging behaviors of butterflies in nature. In this paper, we have developed the Markov chain model of the BOA and analyzed the convergence behavior of the algorithm. The Markov chain model of the BOA is constituted where the population sequence generated by the algorithm is found to be a finite homogenous Markov chain and the defined population state set is found to be reducible. Convergence analysis of the algorithm is performed mathematically using the Markov chain model of the algorithm with the help of global convergence theorem which is based on a random search algorithm satisfying two subtle conditions. The butterfly algorithm has been found to satisfy the conditions for the global convergence theorem to enact, whereof it guarantees the global convergence of the BOA. We have also tried to show experimentally that the convergence of the algorithm does not always have considerable impact on rate of convergence as it is influenced by various other factors. Moreover, the convergence of BOA has been compared with several state-of-the-art algorithms experimentally. Further, the effects of the parameters, namely sensory modality and power exponent on the performance of BOA, have been studied. [ABSTRACT FROM AUTHOR]

This paper describes the first record of the critically endangered gravid female spiny butterfly ray, Gymnura altavela (Linnaeus, 1758), in the northern Mediterranean and simultaneously the only record of the species in this century for the Adriatic Sea. The female (173 cm disc width and 43.25 kg total weight) was captured at c. 200 m, off Vlorë in the southern Adriatic Sea off Albania on 12 May 2022. Upon necropsy, seven near‐term foetuses (mean 328 ± 6.47 mm disc width and mean 355 ± 20.06 g in total weight) were transported for further in‐vitro incubation. These results represent the deepest capture for the species, as well as the largest litter size and largest size at birth in the Mediterranean Sea, which indicates the need for further research. [ABSTRACT FROM AUTHOR]

In our title, "chaos" means there is a positive Lyapunov exponent that causes the tornado to move. We are asserting that a positive Lyapunov exponent is not always needed to have a butterfly effect. Lorenz's butterfly effect initially appeared in meteorology and has captured the imaginations of people for applications to all kinds of fields. We feel it is important to understand simpler non-meteorological models to understand the additional aspects of the butterfly effect. This paper presents simple linear map models that lack "chaos" but exhibit a butterfly effect: our simplest model does not have any positive Lyapunov exponents but still exhibits a butterfly effect, that is, temporary exponential growth from a tiny perturbation such as one infected mosquito setting off an epidemic outbreak. We focus on a 24-dimensional version of the map where a significant butterfly effect is observed even though the only Lyapunov exponent is 0. We introduce a linear "infected mosquito" model that shows how off-diagonal matrix entries can cause a finite-time growth rate. We argue that the degree of instability in our systems can be better measured by its finite-time growth rate. Our findings suggest that even in linear systems, off-diagonal matrix entries can significantly impact the system's behavior and be more important than the Lyapunov exponents in higher-dimensional systems. A focus on finite-time growth rates can yield valuable insights into the system's dynamics. [ABSTRACT FROM AUTHOR]

The optimal configuration problem of distributed generators (DGs) and shunt capacitors (SCs) in a radial distributed network (RDN) is to find the best installation locations and optimal capacities of DGs and SCs for optimizing a certain performance indicator. The discontinuity characteristic and huge computation of optimizing DGs and SCs in RDN make it no longer applicable by traditional methods. In this paper, a new bus processing method and an improved butterfly algorithm are proposed to solve the optimal configuration of DGs and SCs. The method of processing nodes considers not only the power loss index (PLI) but also the voltage amplitude of the original system, forming a sequence of candidate nodes to guide algorithm to optimizing, which can simplify the algorithm search space and improve search efficiency. Meanwhile, the butterfly algorithm with constriction factor (BF-CF) combines the inertia coefficient to introduce a constriction factor, improves the speed update model and the local search pattern, and overcomes the disadvantages of the original butterfly algorithm which is easy to fall into the local optimum and the precision of result is not high. To verify the performance of the proposed method, minimizing the active power loss and voltage deviation are selected as the objective function and reactive power loss and the worst voltage are taken as reference targets, which are performed in three standard test systems of 33-bus, 69-bus and 119-bus, respectively. The simulation results illustrate that, compared with the original butterfly algorithm and other intelligent algorithms, the method proposed in this paper has more obvious advantages and performance in solving the configuration problem of DGs and SCs in systems of various scales. [ABSTRACT FROM AUTHOR]

Lane detection is a technique that uses geometric features as an input to the autonomous vehicle to automatically distinguish lane markings. To process the intricate features present in the lane images, traditional computer vision (CV) techniques are typically time-consuming, need more computing resources, and use complex algorithms. To address this problem, this paper presents a deep convolutional neural network (CNN) architecture that prevents the complexities of traditional CV techniques. CNN is regarded as a reasonable method for lane marking prediction, while improved performance requires hyperparameter tuning. To enhance the initial parameter setting of the CNN, an S-Shaped Binary Butterfly Optimization Algorithm (SBBOA) is utilized in this paper. In this way, the relative parameters of CNN are selected for accurate lane marking. To evaluate the performance of the proposed SBBOA-CNN model, extensive experiments are conducted using the TUSimple and CULane datasets. The experimental results obtained show that the proposed approach outperforms other state-of-the-art techniques in terms of classification accuracy, precision, F1-score, and recall. The proposed model also considerably outperforms the CNN in terms of classification accuracy, average elapsed time, and receiver operating characteristics curve measure. This result demonstrates that the SBBOA optimized CNN exhibits higher robustness and stability than CNN. [ABSTRACT FROM AUTHOR]

Artificial neural network (ANN) which is an information processing technique developed by modeling the nervous system of the human brain is one of the most powerful learning methods today. One of the factors that make ANN successful is its training algorithm. In this paper, an improved butterfly optimization algorithm (IBOA) based on the butterfly optimization algorithm was proposed for training the feed-forward artificial neural networks. The IBOA algorithm has the chaotic property which helps optimization algorithms to explore the search space more dynamically and globally. In the experiments, ten chaotic maps were used. The success of the IBOA algorithm was tested on 13 benchmark functions which are well known to those working on global optimization and are frequently used for testing and analysis of optimization algorithms. The Tent-mapped IBOA algorithm outperformed the other algorithms in most of the benchmark functions. Moreover, the success of the IBOA-MLP algorithm also has been tested on five classification datasets (xor, balloon, iris, breast cancer, and heart) and the IBOA-MLP algorithm was compared with four algorithms in the literature. According to the statistical performance metrics (sensitivity, specificity, precision, F1-score, and Friedman test), the IBOA-MLP outperformed the other algorithms and proved to be successful in training the feed-forward artificial neural networks. [ABSTRACT FROM AUTHOR]

Butterfly Optimization Algorithm (BOA) is a recent metaheuristic that has been used in several optimization problems. In this paper, we propose a new version of the algorithm (xBOA) based on the crossover operator and compare its results to the original BOA and 3 other variants recently introduced in the literature. We also proposed a framework for solving the unknown area exploration problem with energy constraints using metaheuristics in both single- and multi-robot scenarios. This framework allowed us to benchmark the performances of different metaheuristics for the robotics exploration problem. We conducted several experiments to validate this framework and used it to compare the effectiveness of xBOA with well-known metaheuristics used in the literature through 5 evaluation criteria. Although BOA and xBOA are not optimal in all these criteria, we found that BOA can be a good alternative to many metaheuristics in terms of the exploration time, while xBOA is more robust to local optima; has better fitness convergence; and achieves better exploration rates than the original BOA and its other variants. [ABSTRACT FROM AUTHOR]

Fuzzy system is one of the most used systems in the decision-making and classification method as it is easy to understand because the way this system works is closer to how humans think. It is a system that uses human experts to hold the membership values to make decisions. However, it is hard to determine the fuzzy parameter manually in a complex problem, and the process of generating the parameter is called fuzzy modelling. Therefore, an optimization method is needed to solve this issue, and one of the best methods to be applied is Butterfly Optimization Algorithm. In this paper, BOA was improvised by combining this algorithm with Harmony Search (HS) in order to achieve optimal results in fuzzy modelling. The advantages of both algorithms are used to balance the exploration and exploitation in the searching process. Two datasets from UCI machine learning were used: Website Phishing Dataset and Phishing Websites Dataset. As a result, the average accuracy for WPD and PWD was 98.69% and 98.80%, respectively. In conclusion, the proposed method shows promising and effective results compared to other methods. [ABSTRACT FROM AUTHOR]

This paper presents a checklist of the butterfly fauna from the Apuseni Natural Park. The study aimed to inventory the fauna of Lepidoptera and their distribution in the park. In the investigated area, a number of 56 species of Lepidoptera were recorded. The research study of butterfly species in the southeastern area of the Apuseni Natural Park was realized over two years (2021 and 2022). In the researched area, the following species protected by European and/or National Legislation were identified: Parnassius mnemosyne, Neptis sappho, Euphydryas maturna and Euplagia quadripunctaria. Threats to some populations of butterflies were reported, predominantly the loss of preferential habitats. Several measures have been proposed for the conservation of the Lepidoptera species including maintaining preferential habitats, mowing meadows at the right time and moderate grazing of animals. [ABSTRACT FROM AUTHOR]

The diversity of Papilionoidea is still poorly known in much of Central America, especially within families such as Hesperiidae. Honduras remains one of the least studied of all the countries in the region, but efforts are underway to expand on a recent list of species and have already produced noticeable results. This paper discusses an ongoing, long-term assessment that is being conducted at a single site, Emerald Valley Nature Reserve, and provides a list of 704 species recorded to date. Finds of particular significance include the discovery of the recently described Emesis eleanorae Gallardo & Grishin, 2021 (Riodinidae) and at least 111 new records for Honduras. This survey demonstrates the importance of conserving species-rich, Premontane Wet Forest formations which remain under constant threat by human encroachment and ensuing anthropogenic practices. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop some forms of mental strength and indicators for 50m butterfly swimmers, prepare CrossFit exercises for the sample members, and then find out the extent of the impact of these exercises on the selected forms of strength, as well as the selected functional variables and the 50m butterfly swim. The researcher used the experimental approach for the research community. He was chosen by the intentional method from the butterfly event swimmers who represent the national team for the year 2022-2023, which numbered (5) swimmers, and the training curriculum was applied to them for a period of (8) weeks at a rate of (3) units per week, and after conducting tests on the sample and extracting and processing the results Statistically, the researcher concluded that CrossFit training had a positive impact on developing forms of strength and functional indicators, as well as the achievement of 50m butterfly. [ABSTRACT FROM AUTHOR]

Influence maximization aims at the identification of a small group of individuals that may result in the most wide information transmission in social networks. Although greedy-based algorithms can yield reliable solutions, the computational cost is extreme expensive, especially in large-scale networks. Additionally, centrality-based heuristics tend to suffer from the problem of low accuracy. To solve the influence maximization problem in an efficient way, a learning-automata-driven discrete butterfly optimization algorithm (LA-DBOA) mapped into the network topology is proposed in this paper. According to the LA-DBOA framework, a novel encoding mechanism and discrete evolution rules adapted to network topology are presented. By exploiting the asymmetry of social connections, a modified learning automata is adopted to guide the butterfly population toward promising areas. Based on the topological features of the discrete networks, a new local search strategy is conceived to enhance the search performance of the butterflies. Extensive experiments are conducted on six real networks under the independent cascade model; the results demonstrate that the proposed algorithm achieves comparable influence spread to that of CELF and outperforms other classical methods, which proves that the meta-heuristics based on swarm intelligence are effective in solving the influence maximization problem. [ABSTRACT FROM AUTHOR]

Bipartite graphs widely exist in real-world scenarios and model binary relations like host-website, author-paper, and user-product. In bipartite graphs, a butterfly (i.e., $2\times 2$ 2 × 2 bi-clique) is the smallest non-trivial cohesive structure and plays an important role in applications such as anomaly detection. Considerable efforts focus on counting butterflies in static bipartite graphs. However, they suffer from high time and space complexity when the bipartite graph of interest is given as a stream of edges. Although there are methods for approximately counting butterflies from bipartite graph streams, they suffer from either low accuracy or high time complexity. Therefore, it is still a challenge to accurately estimate butterfly counts from bipartite graph streams in a short time. To address this issue, we develop novel algorithms by exploiting the bipartite nature, which subtly integrates sampling and sketching techniques. We provide accurate estimators for butterfly counts and derive simple yet exact formulas for bounding their errors. We also conduct extensive experiments on a variety of real-world large bipartite graphs. Experimental results demonstrate that our algorithms are up to 20.0 times more accurate and up to 286.3 times faster than state-of-the-art methods under the same memory usage. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid sine cosine butterfly optimization algorithm (m-SCBOA), in which a modified butterfly optimization algorithm is combined with sine cosine algorithm to achieve superior exploratory and exploitative search capabilities. The newly suggested m-SCBOA algorithm has been tested on 39 benchmark functions and compared with seven state-of-the-art algorithms. Moreover, it is tested with the CEC 2017 test suite, and the results are compared with seven more algorithms. Experimental results have demonstrated superior results of the proposed algorithm in nearly 75% of occasions on average whereas, similar results in nearly 20% of cases. Simulation results and convergence graphs show the supremacy of the m-SCBOA over the compared algorithms. The Friedman rank test validates that the m-SCBOA is statistically the best among the experiments. Additionally, four real-world engineering design problems and a priori multi-objective problem called parameter optimization of Al–4.5% Cu–TiC metal matrix composite are solved and the outcomes are compared to a wide range of algorithms. The results of these case studies establish the merits of m-SCBOA in solving challenging, real-world problems with unknown global optima. [ABSTRACT FROM AUTHOR]

Indonesia is a mega biodiversity country and is host to many endemic butterfly species. However, data about Indonesian butterflies is still very limited due to some constraints of research personnel capacity, budget, challenging access to many islands, and other factors. This paper aims to inform a new direction using the Kupunesia App as a breakthrough and to invite more citizen science participation to accelerate butterfly research in Indonesia. The involvement of butterfly enthusiasts as citizen scientists has increased the knowledge as can be seen within one month of the launching of the App in November 2022. About 500 people have downloaded the App, 182 people have contributed their data to Kupunesia App, 342 checklists were recorded and 321 species were observed from various locations in Indonesia. This momentum should be maintained so that it can effectively and sustainably support citizen scientists. [ABSTRACT FROM AUTHOR]

Ornithoptera croesus, Wallace's Golden Birdwing butterfly, is one of the most highly-demanded butterflies in the world. Annual trades of this endemic species have been monitored and mechanisms to control the trades have been developed to ensure the sustainability of the species. This paper aims to address and clarify the issues on the trade and threat to the species. The methodology involves the probe into the CITES Trade Database, into the threat to the species based on observation during field surveys, and into the possible solution. The results showed that there were some discrepancies in the data provided by the exporter and importer of the species and that the threats to the species are real. An approach to improve the condition is discussed. The solution to meet the demand for this species is offered through captive breeding and ranching for sustainable use. [ABSTRACT FROM AUTHOR]

The article presents abstracts on biological sciences including the addition of three species of butterflies to Alabama, a study on a vernal pool in Oak Mountain State Park in Shelby County, Alabama, related to the breeding of amphibians and identifying new gene in drosophila.

Many methods achieve the visual object tracking task with deep learning technologies. As the deep features of different levels contain various semantic information and functions, this paper presents a multi-hierarchy feature aggregation approach to tackle the specific issues in the tracking task, which consists of two aspects. On one hand, this paper integrates the features captured by the offline and online classifiers at the score level, which constructs complementary roles of these classifiers to enhance the stability of classification. Besides, the proposed offline classifier is continuously optimized with different levels of features to reinforce classification constraints. On the other hand, we design a butterfly attention module to promote the capacity of multi-hierarchy feature aggregation in the regression network, which aims to fuse and strengthen the multi-scale features by attending to their spatial information. It can capture more spatial contexts by utilizing the self-attention mechanism during the fusion procedure, and preserve the hierarchy of the features during the strengthening process. Extensive experiments on four public datasets, i.e., VOT2018, OTB100, NFS and LaSOT datasets, demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Currently, electrical distribution networks (EDNs) have used modern technologies to operate and serve many types of consumers such as renewable energy, energy storage systems, electric vehicles, and demand response programs. Due to the variability and unpredictability of these technologies, all these technologies have brought various challenges to the operation and control of EDNs. In this case, in order to operate effectively, it is inevitable that effective power redistribution is required in the entire network. In this paper, a multi-objective based dynamic optimal network reconfiguration (DONR) problem is formulated using power loss and voltage deviation index considering the hourly variation of load, photovoltaic (PV) power, and electric vehicle (EV) fleet load in the network. This paper introduces recently introduced meta-heuristic butterfly optimization algorithm (BOA) and it's improve variant of self-adaptive method (SABOA) for solving the DONR problem. The simulation study of IEEE 33-bus EDN under different conditions has proved the effectiveness of DONR, and its adoptability for real-time applications. In addition, by comparing different performance indicators (such as mean, standard deviation, variance, and average calculation time) of 50 independently run simulations, the efficiency of SABOA can be evaluated compared with other heuristic methods (HMs). Comparative studies show that SABOA is better than PSO, TLBO, CSA and FPA in the frequent occurrence of global optimal values. [ABSTRACT FROM AUTHOR]

A broadband coplanar waveguide fed butterfly shape slot antenna is presented in this paper. The designed antenna operates in the frequency range from 1.89 to 5.21 GHz (93.5%) for S11 ≤ −10 dB with a broadside gain of 5 ± 1 dBi over the bandwidth of 1.97–5.19 GHz (90.3%). The designed antenna is uniplanar having the size of 0.5 λ0 × 0.28 λ0 × 0.005 λ0 (λ0 is the wavelength at the lowest operating frequency in free space). The proposed configuration has a stable radiation pattern over the operating frequency band. The designed antenna is fabricated and simulated results are validated with measured results. [ABSTRACT FROM AUTHOR]

• Butterfly detection by Quancttum mechanics adaptation on a multi-layered graph. • Local contrast estimation. • Backgroundw and foreground guidance for butterfly segmentation. • Regional Background connectivity measure. • Diffusion of contrast scores using Schrödinger equation on a multi-layered graph. Butterflies have a prominent role in the agro-ecological ecosystems. Some butterfly populations can injure wildlife, vegetation, and even humans in addition to causing harm to flora and fauna. By contrast, the presence of some other ones can help in improving agricultural productivity and preserving the agro-ecological ecosystems. Butterfly segmentation is therefore an initial process that precedes species recognition. In this paper, we propose a new segmentation process that adapts quantum mechanics to be deployed on a multi-layered graph. To achieve a proper butterfly segmentation, we implement efficiently the Schrödinger equation in a propagation process across the different layers of the graph. Furthermore, It is supported by both background and foreground priors guidance coupled to local contrast information. Comparative evaluation suggests that our method has higher resistance than competing methods to artefacts that are inherent to agro-ecological photographs. Our algorithm shows a considerable advantage over single-layered graph based versions when dealing with some image details. It also outperforms some deep learning based methods that achieve high segmentation performance. Unlike these methods, ours does not involve any training step. Thus, it doesn't require high performance equipments or supplementary human labelling operation and does not fall in the problem of generalization. [Display omitted] [ABSTRACT FROM AUTHOR]

Butterfly Optimisation Algorithm (BOA) is a kind of meta-heuristic swarm intelligence algorithm based on butterfly foraging strategy, but it still needs to be improved in the aspects of convergence speed and accuracy when solving with high-dimensional optimisation problems. In this paper, an improved butterfly optimisation algorithm is proposed, in which guiding weight and population restart strategy are applied to the original algorithm. By adding guiding weight to the global search equation, the convergence speed and accuracy of the algorithm are improved, and the possibility of jumping out of the local optimal solution is increased by the population restart strategy. In order to verify the performance of the proposed algorithm, 24 benchmark functions commonly used for optimisation algorithm experiments are applied in this paper, including 12 unimodal functions and 12 multimodal functions. Experimental results show that the proposed algorithm improves the convergence speed, accuracy and the ability to jump out of the local optimal solution. [ABSTRACT FROM AUTHOR]

Euphydryas aurinia, a highly endangered species listed on the Annex II of the Habitats Directive as well as on the Annex II of the Bern Convention, has a wide distribution in the Palearctic realm. Although it is an intensively studied species, its distribution is still not enough known. In Romania, in the last century it has been reported almost exclusively from the regions inside and westwards to the Carpathian Mountains. Records from Oltenia and Wallachia are filling a gap in the known distribution of this butterfly in the southern part of Romania, while 13 new records consolidate the known range of this species in southern and eastern Transylvania. In order to assess the progress encountered, a critical reviewed checklist with the records of the Marsh Fritillary presented in a regional distribution map complete this paper. [ABSTRACT FROM AUTHOR]

The present paper is a continuation of the previously published spring butterflies of the Eastern Mediterranean Region. The information of collection station of the previously listed species of 68 stations was included in this study. The stations were divided into 8 groups according to their vertical distribution. Habitat types at various altitudes have been defined. The communities of Lepidoptera detected in these habitats are given. The status of the Pontia daplidice/edusa species group in the region is discussed. [ABSTRACT FROM AUTHOR]

In the present times, artificial-intelligence based techniques are considered as one of the prominent ways to classify images which can be conveniently leveraged in the real-world scenarios. This technology can be extremely beneficial to the lepidopterists, to assist them in classification of the diverse species of Rhopalocera, commonly called as butterflies. In this article, image classification is performed on a dataset of various butterfly species, facilitated via the feature extraction process of the Convolutional Neural Network (CNN) along with leveraging the additional features calculated independently to train the model. The classification models deployed for this purpose predominantly include K-Nearest Neighbors (KNN), Random Forest and Support Vector Machine (SVM). However, each of these methods tend to focus on one specific class of features. Therefore, an ensemble of multiple classes of features used for classification of images is implemented. This research paper discusses the results achieved from the classification performed on basis of two different classes of features i.e., structure and texture. The amalgamation of the two specified classes of features forms a combined data set, which has further been used to train the Growing Convolutional Neural Network (GCNN), resulting in higher accuracy of the classification model. The experiment performed resulted in promising outcomes with TP rate, FP rate, Precision, recall and F-measure values as 0.9690, 0.0034, 0.9889, 0.9692 and 0.9686 respectively. Furthermore, an accuracy of 96.98% was observed by the proposed methodology. [ABSTRACT FROM AUTHOR]