An evolutionary innovation for mating facilitates ecological niche expansion and buffers species against climate change.

One of the drivers of life's diversification has been the emergence of "evolutionary innovations": The evolution of traits that grant access to underused ecological niches. Since ecological interactions can occur separately from mating, mating-related traits have not traditionally been considered factors in niche evolution. However, in order to persist in their environment, animals need to successfully mate just as much as they need to survive. Innovations that facilitate mating activity may therefore be an overlooked determinant of species' ecological limits. Here, we show that species' historical niches and responses to contemporary climate change are shaped by an innovation involved in mating-a waxy, ultra-violet-reflective pruinescence produced by male dragonflies. Physiological experiments in two species demonstrate that pruinescence reduces heating and water loss. Phylogenetic analyses show that pruinescence is gained after taxa begin adopting a thermohydrically stressful mating behavior. Further comparative analyses reveal that pruinose species are more likely to breed in exposed, open-canopy microhabitats. Biogeographic analyses uncover that pruinose species occupy warmer and drier regions in North America. Citizen-science observations of Pachydiplax longipennis suggest that the extent of pruinescence can be optimized to match the local conditions. Finally, temporal analyses indicate that pruinose species have been buffered against contemporary climate change. Overall, these historical and contemporary patterns show that successful mating can shape species' niche limits in the same way as growth and survival.

Molecular mechanisms underlying metamorphosis in the most-ancestral winged insect.

Insects comprise over half of the described species, and the acquisition of metamorphosis must have contributed to their diversity and prosperity. The order Odonata (dragonflies and damselflies) is among the most-ancestral insects with drastic morphological changes upon metamorphosis, in which understanding of the molecular mechanisms will provide insight into the evolution of incomplete and complete metamorphosis in insects. In order to identify metamorphosis-related genes in Odonata, we performed comprehensive RNA-sequencing of the blue-tailed damselfly Ischnura senegalensis at different developmental stages. Comparative RNA-sequencing analyses between nymphs and adults identified eight nymph-specific and seven adult-specific transcripts. RNA interference (RNAi) of these candidate genes demonstrated that three transcription factors, Krüppel homolog 1 (Kr-h1), broad, and E93 play important roles in metamorphosis of both I. senegalensis and a phylogenetically distant dragonfly, Pseudothemis zonataE93 is essential for adult morphogenesis, and RNAi of Kr-h1 induced precocious metamorphosis in epidermis via up-regulation of E93 Precocious metamorphosis was also induced by RNAi of the juvenile hormone receptor Methoprene-tolerant (Met), confirming that the regulation of metamorphosis by the MEKRE93 (Met-Kr-h1-E93) pathway is conserved across diverse insects including the basal insect lineage Odonata. Notably, RNAi of broad produced unique grayish pigmentation on the nymphal abdominal epidermis. Survey of downstream genes for Kr-h1, broad, and E93 uncovered that unlike other insects, broad regulates a substantial number of nymph-specific and adult-specific genes independently of Kr-h1 and E93 These findings highlight the importance of functional changes and rewiring of the transcription factors Kr-h1, broad, and E93 in the evolution of insect metamorphosis.

Oxygen extraction efficiency of the tidally-ventilated rectal gills of dragonfly nymphs.

Dragonfly nymphs breathe water using tidal ventilation, a highly unusual strategy in water-breathing animals owing to the high viscosity, density and low oxygen (O2) concentration of water. This study examines how well these insects extract O2 from the surrounding water during progressive hypoxia. Nymphs were attached to a custom-designed respiro-spirometer to simultaneously measure tidal volume, ventilation frequency and metabolic rate. Oxygen extraction efficiencies (OEE) were calculated across four partial pressure of oxygen (pO2) treatments, from normoxia to severe hypoxia. While there was no significant change in tidal volume, ventilation frequency increased significantly from 9.4 ± 1.2 breaths per minute (BPM) at 21.3 kPa to 35.6 ± 2.9 BPM at 5.3 kPa. Metabolic rate increased significantly from 1.4 ± 0.3 µl O2 min-1 at 21.3 kPa to 2.1 ± 0.4 µl O2 min-1 at 16.0 kPa, but then returned to normoxic levels as O2 levels declined further. OEE of nymphs was 40.1 ± 6.1% at 21.3 kPa, and did not change significantly during hypoxia. Comparison to literature shows that nymphs maintain their OEE during hypoxia unlike other aquatic tidal-breathers and some unidirectional breathers. This result, and numerical models simulating experimental conditions, indicate that nymphs maintain these extraction efficiencies by increasing gill conductance and/or lowering internal pO2 to maintain a sufficient diffusion gradient across their respiratory surface.

How to train your dragon: absolute conditioning in larval dragonflies.

Insects, despite possessing relatively small brains, exhibit noteworthy adaptive behaviors, making them intriguing subjects for understanding learning mechanisms. This study explores the learning capabilities of dragonfly larvae (Anisoptera: Aeshnidae) in conditioning experiments, shedding light on the cognitive processes that underpin their remarkable abilities. As apex predators, dragonflies play a crucial role in ecosystems, necessitating a diverse range of learning behaviors for survival and reproductive success. We addressed whether dragonfly larvae can differentiate between different colored stimuli and associate color with prey. Our experimental design demonstrated that dragonfly larvae are able to recognize conditioning stimuli. The findings contribute valuable insights into the cognitive abilities of dragonflies, suggesting that these insects can learn and discriminate colors of stimuli. Overall, this research broadens our understanding of insect learning and cognition, contributing to the broader field of animal behavior and memory.

Enhancing effort estimation in global software development using a unique combination of Neuro Fuzzy Logic and Deep Learning Neural Networks (NFDLNN).

Effective project planning and management in the global software development landscape relies on addressing major issues like cost estimation and effort allocation. Timely estimation of software development is a critical focus in software engineering research. With the industry increasingly relying on diverse teams worldwide, accurate estimation becomes vital. Software size serves as a common measure for costs and schedules, but advanced estimation methods consider various variables, such as project purpose, personnel expertise, time and efficiency constraints, and technology requirements. Estimating software costs involve significant financial and strategic commitments, making it crucial to address complexity and versatility related to cost drivers. To achieve enhanced accuracy and convergence, we employ the cuckoo algorithm in our proposed NFDLNN (Neuro Fuzzy Logic and Deep Learning Neural Networks) model. Through extensive validation with industrial project data, using Function Point Analysis as the algorithmic models, our NFA model demonstrates high accuracy in software cost approximation, outperforming existing methods insights of MRE of 3.33, BRE of 0.13, and PI of 74.48. Our research contributes to improved project planning and decision-making processes in global software development endeavours.

Sparse spike trains and the limitation of rate codes underlying rapid behaviours.

Animals live in dynamic worlds where they use sensorimotor circuits to rapidly process information and drive behaviours. For example, dragonflies are aerial predators that react to movements of prey within tens of milliseconds. These pursuits are likely controlled by identified neurons in the dragonfly, which have well-characterized physiological responses to moving targets. Predominantly, neural activity in these circuits is interpreted in context of a rate code, where information is conveyed by changes in the number of spikes over a time period. However, such a description of neuronal activity is difficult to achieve in real-world, real-time scenarios. Here, we contrast a neuroscientists' post-hoc view of spiking activity with the information available to the animal in real-time. We describe how performance of a rate code is readily overestimated and outline a rate code's significant limitations in driving rapid behaviours.

Survey of the Prosthogonimus spp. metacercariae infection in the second intermediate host dragonfly in Heilongjiang Province, China.

Prosthogonimiasis poses a threat to the reproductive system of poultry and wild birds, which are the definitive hosts of the parasite causing this disease. However, the parasite infection of the second intermediate host (dragonfly), the primary vector of this pathogen, is rarely reported. In this study, the prevalence of Prosthogonimus infection in dragonflies was investigated from June 2019 to October 2022 in Heilongjiang Province, northeast China. The species of metacercariae isolated from dragonfly were identified by morphological characteristics, molecular biology techniques, and animal infection experiments. The results showed that 11 species of dragonflies and one damselfly were identified and among six of the dragonflies infected by Prosthogonimus metacercariae, Sympetrum depressiusculum (28.53%) had the highest infection rate among all positive dragonflies, followed by Sympetrum vulgatum (27.86%) and Sympetrum frequens (20.99%), which are preferred hosts, and the total prevalence was 20.39% (2061/10,110) in Heilongjiang Province. Three species of Prosthogoniumus metacercariae were isolated, including Prosthogonimus cuneatus, Prosthogonimus pullucidus, and Prosthogonimus sp., among which P. cuneatus was the dominant species in dragonflies in Heilongjiang Province. This is the first report on the prevalence of Prosthogonimus in dragonflies in China, which provides baseline data for the control of prosthogonimiasis in Heilongjiang Province and a reference for the prevention of prosthogonimiasis in other areas of China.

Enhancing Tool Wear Prediction Accuracy Using Walsh-Hadamard Transform, DCGAN and Dragonfly Algorithm-Based Feature Selection.

Tool wear is an important concern in the manufacturing sector that leads to quality loss, lower productivity, and increased downtime. In recent years, there has been a rise in the popularity of implementing TCM systems using various signal processing methods and machine learning algorithms. In the present paper, the authors propose a TCM system that incorporates the Walsh-Hadamard transform for signal processing, DCGAN aims to circumvent the issue of the availability of limited experimental dataset, and the exploration of three machine learning models: support vector regression, gradient boosting regression, and recurrent neural network for tool wear prediction. The mean absolute error, mean square error and root mean square error are used to assess the prediction errors from three machine learning models. To identify these relevant features, three metaheuristic optimization feature selection algorithms, Dragonfly, Harris hawk, and Genetic algorithms, were explored, and prediction results were compared. The results show that the feature selected through Dragonfly algorithms exhibited the least MSE (0.03), RMSE (0.17), and MAE (0.14) with a recurrent neural network model. By identifying the tool wear patterns and predicting when maintenance is required, the proposed methodology could help manufacturing companies save money on repairs and replacements, as well as reduce overall production costs by minimizing downtime.

Blockchain and Machine Learning Inspired Secure Smart Home Communication Network.

With the increasing growth rate of smart home devices and their interconnectivity via the Internet of Things (IoT), security threats to the communication network have become a concern. This paper proposes a learning engine for a smart home communication network that utilizes blockchain-based secure communication and a cloud-based data evaluation layer to segregate and rank data on the basis of three broad categories of Transactions (T), namely Smart T, Mod T, and Avoid T. The learning engine utilizes a neural network for the training and classification of the categories that helps the blockchain layer with improvisation in the decision-making process. The contributions of this paper include the application of a secure blockchain layer for user authentication and the generation of a ledger for the communication network; the utilization of the cloud-based data evaluation layer; the enhancement of an SI-based algorithm for training; and the utilization of a neural engine for the precise training and classification of categories. The proposed algorithm outperformed the Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system, the data fusion technique, and artificial intelligence Internet of Things technology in providing electronic information engineering and analyzing optimization schemes in terms of the computation complexity, false authentication rate, and qualitative parameters with a lower average computation complexity; in addition, it ensures a secure, efficient smart home communication network to enhance the lifestyle of human beings.

A Novel A-ECMS Energy Management Strategy Based on Dragonfly Algorithm for Plug-in FCEVs.

The mechanical coupling of multiple powertrain components makes the energy management of 4-wheel-drive (4WD) plug-in fuel cell electric vehicles (PFCEVs) relatively complex. Optimizing energy management strategies (EMSs) for this complex system is essential, aiming at improving the vehicle economy and the adaptability of operating conditions. Accordingly, a novel adaptive equivalent consumption minimization strategy (A-ECMS) based on the dragonfly algorithm (DA) is proposed to achieve coordinated control of the powertrain components, front and rear motors, as well as the fuel cell system and the battery. To begin with, the equivalent consumption minimization strategy (ECMS) with extraordinary instantaneous optimization ability is used to distribute the vehicle demand power into the front and rear motor power, considering the different motor characteristics. Subsequently, under the proposed novel hierarchical energy management framework, the well-designed A-ECMS based on DA empowers PFCEVs with significant energy-saving advantages and adaptability to operating conditions, which are achieved by precise power distribution considering the operating characteristics of the fuel cell system and battery. These provide state-of-the-art energy-saving abilities for the multi-degree-of-freedom systems of PFCEVs. Lastly, a series of detailed evaluations are performed through simulations to validate the improved performance of A-ECMS. The corresponding results highlight the optimal control performance in the energy-saving performance of A-ECMS.

Joint User Association and Deployment Optimization for Energy-Efficient Heterogeneous UAV-Enabled MEC Networks.

Unmanned aerial vehicles (UAVs) providing additional on-demand communication and computing services have become a promising technology. However, the limited energy supply of UAVs, which constrains their service duration, has emerged as an obstacle in UAV-enabled networks. In this context, a novel task offloading framework is proposed in UAV-enabled mobile edge computing (MEC) networks. Specifically, heterogeneous UAVs with different communication and computing capabilities are considered and the energy consumption of UAVs is minimized via jointly optimizing user association and UAV deployment. The optimal transport theory is introduced to analyze the user association sub-problem, and the UAV deployment for each sub-region is determined by a dragonfly algorithm (DA). Simulation results show that the energy consumption performance is significantly improved by the proposed algorithm.

Transcatheter mitral valve repair in patient with atrial functional mitral regurgitation using novel DragonFly™ device.

We report the first case of transcatheter mitral valve repair with the novel DragonFly™ device, a transcatheter edge-to-edge mitral regurgitation (MR) repair device, in a patient with severe, symptomatic MR due to annular dilation from atrial functional disease (Carpentier type I). The patient had experienced multiple heart failure events and was unsuitable for surgery due to pulmonary dysfunction and obesity.

A Hybrid Dragonfly Algorithm for Efficiency Optimization of Induction Motors.

Induction motors tend to have better efficiency on rated conditions, but at partial load conditions, when these motors operate on rated flux, they exhibit lower efficiency. In such conditions, when these motors operate for a long duration, a lot of electricity gets consumed by the motors, due to which the computational cost as well as the total running cost of industrial plant increases. Squirrel-cage induction motors are widely used in industries due to their low cost, robustness, easy maintenance, and good power/mass relation all through their life cycle. A significant amount of electrical energy is consumed due to the large count of operational units worldwide; hence, even an enhancement in minute efficiency can direct considerable contributions within revenue saving, global electricity consumption, and other environmental facts. In order to improve the efficiency of induction motors, this research paper presents a novel contribution to maximizing the efficiency of induction motors. As such, a model of induction motor drive is taken, in which the proportional integral (PI) controller is tuned. The optimal tuning of gains of a PI controller such as proportional gain and integral gain is conducted. The tuning procedure in the controller is performed in such a condition that the efficiency of the induction motor should be maximum. Moreover, the optimization concept relies on the development of a new hybrid algorithm, the so-called Scrounger Strikes Levy-based dragonfly algorithm (SL-DA), that hybridizes the concept of dragonfly algorithm (DA) and group search optimization (GSO). The proposed algorithm is compared with particle swarm optimization (PSO) for verification. The analysis of efficiency, speed, torque, energy savings, and output power is validated, which confirms the superior performance of the suggested method over the comparative algorithms employed.

Dragondrop: a novel passive mechanism for aerial righting in the dragonfly.

Dragonflies perform dramatic aerial manoeuvres when chasing targets but glide for periods during cruising flights. This makes dragonflies a great system to explore the role of passive stabilizing mechanisms that do not compromise manoeuvrability. We challenged dragonflies by dropping them from selected inverted attitudes and collected 6-degrees-of-freedom aerial recovery kinematics via custom motion capture techniques. From these kinematic data, we performed rigid-body inverse dynamics to reconstruct the forces and torques involved in righting behaviour. We found that inverted dragonflies typically recover themselves with the shortest rotation from the initial body inclination. Additionally, they exhibited a strong tendency to pitch-up with their head leading out of the manoeuvre, despite the lower moment of inertia in the roll axis. Surprisingly, anaesthetized dragonflies could also complete aerial righting reliably. Such passive righting disappeared in recently dead dragonflies but could be partially recovered by waxing their wings to the anaesthetised posture. Our kinematics data, inverse dynamics model and wind-tunnel experiments suggest that the dragonfly's long abdomen and wing posture generate a rotational tendency and passive attitude recovery mechanism during falling. This work demonstrates an aerodynamically stable body configuration in a flying insect and raises new questions in sensorimotor control for small flying systems.

Phylogeny and classification of Odonata using targeted genomics.

Dragonflies and damselflies are a charismatic, medium-sized insect order (~6300 species) with a unique potential to approach comparative research questions. Their taxonomy and many ecological traits for a large fraction of extant species are relatively well understood. However, until now, the lack of a large-scale phylogeny based on high throughput data with the potential to connect both perspectives has precluded comparative evolutionary questions for these insects. Here, we provide an ordinal hypothesis of classification based on anchored hybrid enrichment using a total of 136 species representing 46 of the 48 families or incertae sedis, and a total of 478 target loci. Our analyses recovered the monophyly for all three suborders: Anisoptera, Anisozygoptera and Zygoptera. Although the backbone of the topology was reinforced and showed the highest support values to date, our genomic data was unable to stronglyresolve portions of the topology. In addition, a quartet sampling approach highlights the potential evolutionary scenarios that may have shaped evolutionary phylogeny (e.g., incomplete lineage sorting and introgression) of this taxon. Finally, in light of our phylogenomic reconstruction and previous morphological and molecular information we proposed an updated odonate classification and define five new families (Amanipodagrionidae fam. nov., Mesagrionidae fam. nov., Mesopodagrionidae fam. nov., Priscagrionidae fam. nov., Protolestidae fam. nov.) and reinstate another two (Rhipidolestidae stat. res., Tatocnemididae stat. res.). Additionally, we feature the problematic taxonomic groupings for examination in future studies to improve our current phylogenetic hypothesis.

Gene selection using hybrid dragonfly black hole algorithm: A case study on RNA-seq COVID-19 data.

This paper introduces a new hybrid approach (DBH) for solving gene selection problem that incorporates the strengths of two existing metaheuristics: binary dragonfly algorithm (BDF) and binary black hole algorithm (BBHA). This hybridization aims to identify a limited and stable set of discriminative genes without sacrificing classification accuracy, whereas most current methods have encountered challenges in extracting disease-related information from a vast amount of redundant genes. The proposed approach first applies the minimum redundancy maximum relevancy (MRMR) filter method to reduce the dimensionality of feature space and then utilizes the suggested hybrid DBH algorithm to determine a smaller set of significant genes. The proposed approach was evaluated on eight benchmark gene expression datasets, and then, was compared against the latest state-of-art techniques to demonstrate algorithm efficiency. The comparative study shows that the proposed approach achieves a significant improvement as compared with existing methods in terms of classification accuracy and the number of selected genes. Moreover, the performance of the suggested method was examined on real RNA-Seq coronavirus-related gene expression data of asthmatic patients for selecting the most significant genes in order to improve the discriminative accuracy of angiotensin-converting enzyme 2 (ACE2). ACE2, as a coronavirus receptor, is a biomarker that helps to classify infected patients from uninfected in order to identify subgroups at risk for COVID-19. The result denotes that the suggested MRMR-DBH approach represents a very promising framework for finding a new combination of most discriminative genes with high classification accuracy.

Impacts of predator-induced behavioural plasticity on the temperature dependence of predator-prey activity and population dynamics.

Predation is a key ecological interaction affecting populations and communities. Climate warming can modify this interaction both directly by the kinetic effects of temperature on biological rates and indirectly through integrated behavioural and physiological responses of the predators and prey. Temperature dependence of predation rates can further be altered by predator-induced plasticity of prey locomotor activity, but empirical data about this effect are lacking. We propose a general framework to understand the influence of predator-induced developmental plasticity on behavioural thermal reaction norms in prey and their consequences for predator-prey dynamics. Using a mesocosm experiment with dragonfly larvae (predator) and newt larvae (prey), we tested if the predator-induced plasticity alters the elevation or the slope of the thermal reaction norms for locomotor activity metrics in prey. We also estimated the joint predator-prey thermal response in mean locomotor speed, which determines prey encounter rate, and modelled the effect of both phenomena on predator-prey population dynamics. Thermal reaction norms for locomotor activity in prey were affected by predation risk cues but with minor influence on the joint predator-prey behavioural response. We found that predation risk cues significantly decreased the intercept of thermal reaction norm for total activity rate (i.e. all body movements) but not the other locomotor activity metrics in the prey, and that prey locomotor activity rate and locomotor speed increased with prey density. Temperature had opposite effects on the mean relative speed of predator and prey as individual speed increased with temperature in predators but decreased in prey. This led to a negligible effect of body temperature on predicted prey encounter rates and predator-prey dynamics. The behavioural component of predator-prey interaction varied much more between individuals than with temperature and the presence of predation risk cues in our system. We conclude that within-population variation in locomotor activity can buffer the influence of body temperature and predation risk cues on predator-prey interactions, and further research should focus on the magnitude and sources of behavioural variation in interacting species to predict the impact of climate change on predator-prey interactions and food web dynamics.

Ontogenetic colour change of a sexual ornament in males of a damselfly: female mimicry, crypsis or both?

Female mimicry by males is a widespread phenomenon in several taxa and may be involved in aggression avoidance or facilitated access to resources. In early developmental stages, female mimicry may be a mechanism involved in signalling sexual immaturity or, when coupled with strategies related to visual camouflage, may be involved in the avoidance of male-male agonistic interactions. Here, we addressed whether the delayed colour maturation of a sexual ornament in males of Mnesarete pudica damselflies might be a case of crypsis, female mimicry or both. We analysed how conspecifics and predators perceive the pigmented wings of juvenile males by contrasting the wing spectra against a savannah background and the wings of both juvenile and sexually mature males and females. Our results based on the modelled visual system of conspecifics and predators suggest that the colour maturation of juvenile males may function as both crypsis and female mimicry. We discuss whether these results related to age- and sexual-dichromatism might be a mechanism to avoid unwanted intraspecific interactions or to avoid territorial and aggressive males. We conclude that the female mimicry and crypsis in juvenile males of M. pudica are mechanisms involved in avoidance of predators and unwanted intraspecific interactions, and the signalling of sexual maturity.

Fecal Microflora from Dragonflies and Its Microorganisms Producing Carotenoids.

The intestines of insects are assumed to be the niche of various microbial groups, and a unique microflora could be formed under environmental conditions different from mammalian intestinal tracts. This chapter describes the bacterial flora formed in the intestines of two dragonfly species, "akatombo" (the red dragonfly; Sympetrum frequens) and "usubaki-tombo" (Pantala flavescens), which fly over a long distance, and carotenoid-producing microorganisms isolated from this flora. C30 carotenoids, which were produced by a bacterium Kurthia gibsonii isolated from S. frequens, were structurally determined.

Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications.

Swarm intelligence is a discipline which makes use of a number of agents for solving optimization problems by producing low cost, fast and robust solutions. The dragonfly algorithm (DA), a recently proposed swarm intelligence algorithm, is inspired by the dynamic and static swarming behaviors of dragonflies, and it has been found to have a higher performance in comparison to other swarm intelligence and evolutionary algorithms in numerous applications. There are only a few surveys about the dragonfly algorithm, and we have found that they are limited in certain aspects. Hence, in this paper, we present a more comprehensive survey about DA, its applications in various domains, and its performance as compared to other swarm intelligence algorithms. We also analyze the hybrids of DA, the methods they employ to enhance the original DA, their performance as compared to the original DA, and their limitations. Moreover, we categorize the hybrids of DA according to the type of problem that they have been applied to, their objectives, and the methods that they utilize.