The genome sequence of the Elephant Hawk-moth, Deilephila elpenor (Linnaeus, 1758).

We present a genome assembly from an individual female Deilephila elpenor (the Elephant Hawk-moth; Arthropoda; Insecta; Lepidoptera; Sphingidae). The genome sequence is 414.1 megabases in span. Most of the assembly is scaffolded into 30 chromosomal pseudomolecules, including the Z and W sex chromosomes. The mitochondrial genome has also been assembled and is 15.37 kilobases in length. Gene annotation of this assembly on Ensembl identified 11,748 protein coding genes.

The evolutionary consequences of learning under competition.

Learning is a taxonomically widespread process by which animals change their behavioural responses to stimuli as a result of experience. In this way, it plays a crucial role in the development of individual behaviour and underpins substantial phenotypic variation within populations. Nevertheless, the impact of learning in social contexts on evolutionary change is not well understood. Here, we develop game theoretical models of competition for resources in small groups (e.g. producer-scrounger and hawk-dove games) in which actions are controlled by reinforcement learning and show that biases in the subjective valuation of different actions readily evolve. Moreover, in many cases, the convergence stable levels of bias exist at fitness minima and therefore lead to disruptive selection on learning rules and, potentially, to the evolution of genetic polymorphisms. Thus, we show how reinforcement learning in social contexts can be a driver of evolutionary diversification. In addition, we consider the evolution of ability in our games, showing that learning can also drive disruptive selection on the ability to perform a task.

Game theory elucidates how competitive dynamics mediate animal social networks.

BACKGROUND: While most game theoretical models assume that individuals randomly interact with all other group members, strong evidence indicates that individuals tend to preferentially interact with some of them. The position of an individual in a network affects, among other factors related to survival, its predation risk and competitive success. Here I then modified the Hawk-Dove game to explore the effect of social network structure on competitive strategy of individuals that differ in their fighting ability and may adjust their use of the Hawk, Dove and Assessor tactics to maximize their foraging success when they meet opponents they are connected with. RESULTS: From randomly generated networks, I demonstrate that phenotypic assortment by fighting ability reduces individuals' aggressiveness and, as such, favours cooperative interactions. Furthermore, the success of individuals with the weakest fighting ability is usually highest within networks where they most frequently meet opponents with the same fighting ability as their own, suggesting they might benefit from breaking connections with strong contestants. This might be the case when strong contestants systematically rely on the aggressive Hawk tactic or the risk of being predated is low and independent of the number of neighbours. Thus, I extended the model and built a dynamic model to allow individuals not only to adjust their behaviour to local conditions but also to modify the structure of the social network. The number of connections and degree of phenotypic assortment are then affected by ecological factors (e.g. resources value and predation risk), but above all by whether individuals can reliably assess the competitive ability of their opponents and adjust their behaviour accordingly. CONCLUSIONS: These findings provide strong evidence that behaviour can play a key role in shaping network structure and highlight the importance of considering the coevolution of network and behaviour to apprehend its consequences on population dynamics.

HAWKFOG-an enhanced deep learning framework for the Fog-IoT environment.

Cardiac disease is considered as the one of the deadliest diseases that constantly increases the globe's mortality rate. Since a lot of expertise is required for an accurate prediction of heart disease, designing an intelligent predictive system for cardiac diseases remains to be complex and tricky. Internet of Things based health regulation systems are a relatively recent technology. In addition, novel Edge and Fog device concepts are presented to advance prediction results. However, the main problem with the current systems is that they are unable to meet the demands of effective diagnosis systems due to their poor prediction capabilities. To overcome this problem, this research proposes a novel framework called HAWKFOGS which innovatively integrates the deep learning for a practical diagnosis of cardiac problems using edge and fog computing devices. The current datasets were gathered from different subjects using IoT devices interfaced with the electrocardiography and blood pressure sensors. The data are then predicted as normal and abnormal using the Logistic Chaos based Harris Hawk Optimized Enhanced Gated Recurrent Neural Networks. The ablation experiments are carried out using IoT nodes interfaced with medical sensors and fog gateways based on Embedded Jetson Nano devices. The suggested algorithm's performance is measured. Additionally, Model Building Time is computed to validate the suggested model's response. Compared to the other algorithms, the suggested model yielded the best results in terms of accuracy (99.7%), precision (99.65%), recall (99.7%), specificity (99.7%). F1-score (99.69%) and used the least amount of Model Building Time (1.16 s) to predict cardiac diseases.

An automated ensemble approach using Harris Hawk optimization for visually evoked EEG signal classification.

Steady-state visually evoked potential is one of the active explorations in the brain-computer interface research. Electroencephalogram based brain computer interface studies have been widely applied to perceive solutions for real-world problems in the healthcare domain. The classification of externally bestowed visual stimuli of different frequencies on a human was experimented to identify the need of paralytic people. Although many classifiers are at the fingertip of machine learning technology, recent research has proven that ensemble learning is more efficacious than individual classifiers. Despite its efficiency, ensemble learning technology exhibits certain drawbacks like taking more time on selecting the optimal classifier subset. This research article utilizes the Harris Hawk Optimization algorithm to select the best classifier subset from the given set of classifiers. The objective of the research is to develop an efficient multi-classifier model for electroencephalogram signal classification. The proposed model utilizes the Boruta Feature Selection algorithm to select the prominent features for classification. Thus selected prominent features are fed into the multi-classifier subset which has been generated by the Harris Hawk Optimization algorithm. The results of the multi-classifier ensemble model are aggregated using Stacking, Bagging, Boosting, and Voting. The proposed model is evaluated against the acquired dataset and produces a promising accuracy of 96.1%, 98.7%, 91.91%, and 99.01% with the ensemble techniques respectively. The proposed model is also validated with other performance metrics such as sensitivity, specificity, and F1-Score. The experimental results show that the proposed model proves its supremacy in segregating the multi-class classification problem with high accuracy.

HHO optimized support vector machine classifier for traditional Chinese medicine syndrome differentiation of diabetic retinopathy.

AIM: To develop a classifier for traditional Chinese medicine (TCM) syndrome differentiation of diabetic retinopathy (DR), using optimized machine learning algorithms, which can provide the basis for TCM objective and intelligent syndrome differentiation. METHODS: Collated data on real-world DR cases were collected. A variety of machine learning methods were used to construct TCM syndrome classification model, and the best performance was selected as the basic model. Genetic Algorithm (GA) was used for feature selection to obtain the optimal feature combination. Harris Hawk Optimization (HHO) was used for parameter optimization, and a classification model based on feature selection and parameter optimization was constructed. The performance of the model was compared with other optimization algorithms. The models were evaluated with accuracy, precision, recall, and F1 score as indicators. RESULTS: Data on 970 cases that met screening requirements were collected. Support Vector Machine (SVM) was the best basic classification model. The accuracy rate of the model was 82.05%, the precision rate was 82.34%, the recall rate was 81.81%, and the F1 value was 81.76%. After GA screening, the optimal feature combination contained 37 feature values, which was consistent with TCM clinical practice. The model based on optimal combination and SVM (GA_SVM) had an accuracy improvement of 1.92% compared to the basic classifier. SVM model based on HHO and GA optimization (HHO_GA_SVM) had the best performance and convergence speed compared with other optimization algorithms. Compared with the basic classification model, the accuracy was improved by 3.51%. CONCLUSION: HHO and GA optimization can improve the model performance of SVM in TCM syndrome differentiation of DR. It provides a new method and research idea for TCM intelligent assisted syndrome differentiation.

Cohesin Complex: Structure and Principles of Interaction with DNA.

Accurate duplication and separation of long linear genomic DNA molecules is associated with a number of purely mechanical problems. SMC complexes are key components of the cellular machinery that ensures decatenation of sister chromosomes and compaction of genomic DNA during division. Cohesin, one of the essential eukaryotic SMC complexes, has a typical ring structure with intersubunit pore through which DNA molecules can be threaded. Capacity of cohesin for such topological entrapment of DNA is crucial for the phenomenon of post-replicative association of sister chromatids better known as cohesion. Recently, it became apparent that cohesin and other SMC complexes are, in fact, motor proteins with a very peculiar movement pattern leading to formation of DNA loops. This specific process has been called loop extrusion. Extrusion underlies multiple functions of cohesin beyond cohesion, but molecular mechanism of the process remains a mystery. In this review, we summarized the data on molecular architecture of cohesin, effect of ATP hydrolysis cycle on this architecture, and known modes of cohesin-DNA interactions. Many of the seemingly disparate facts presented here will probably be incorporated in a unified mechanistic model of loop extrusion in the not-so-distant future.

Evaluation of student failure in higher education by an innovative strategy of fuzzy system combined optimization algorithms and AI.

This research suggests two novel metaheuristic algorithms to enhance student performance: Harris Hawk's Optimizer (HHO) and the Earthworm Optimization Algorithm (EWA). In this sense, a series of adaptive neuro-fuzzy inference system (ANFIS) proposed models were trained using these methods. The selection of the best-fit model depends on finding an excellent connection between inputs and output(s) layers in training and testing datasets (e.g., a combination of expert knowledge, experimentation, and validation techniques). The study's primary result is a division of the participants into two performance-based groups (failed and non-failed). The experimental data used to build the models measured fourteen process variables: relocation, gender, age at enrollment, debtor, nationality, educational special needs, current tuition fees, scholarship holder, unemployment, inflation, GDP, application order, day/evening attendance, and admission grade. During the model evaluation, a scoring system was created in addition to using mean absolute error (MAE), mean squared error (MSE), and area under the curve (AUC) to assess the efficacy of the utilized approaches. Further research revealed that the HHO-ANFIS is superior to the EWA-ANFIS. With AUC = 0.8004 and 0.7886, MSE of 0.62689 and 0.65598, and MAE of 0.64105 and 0.65746, the failure of the pupils was assessed with the most significant degree of accuracy. The MSE, MAE, and AUC precision indicators showed that the EWA-ANFIS is less accurate, having MSE amounts of 0.71543 and 0.71776, MAE amounts of 0.70819 and 0.71518, and AUC amounts of 0.7565 and 0.758. It was found that the optimization algorithms have a high ability to increase the accuracy and performance of the conventional ANFIS model in predicting students' performance, which can cause changes in the management of the educational system and improve the quality of academic programs.

Feeding en route: Prey availability and traits influence prey selection by an avian predator on migration.

During animal migration, ephemeral communities of taxa at all trophic levels co-occur over space and time. The interactions between predators and prey along migration corridors are ecologically and evolutionarily significant. However, these interactions remain understudied in terrestrial systems and warrant further investigations using novel approaches. We investigated the predator-prey interactions between a migrating avivorous predator and ephemeral avian prey community in the fall migration season. We tested for associations between avian traits and prey selection and hypothesized that prey traits (i.e. relative size, flocking behaviour, habitat, migration tendency and availability) would influence prey selection by a sexually dimorphic raptor on migration. To document prey consumption, we sampled trace prey DNA from beaks and talons of migrating sharp-shinned hawks Accipiter striatus (n = 588). We determined prey availability in the ephemeral avian community by extracting weekly abundance indices from eBird Status and Trends data. We used discrete choice models to assess prey selection and visualized the frequency of prey in diet and availability on the landscape over the fall migration season. Using eDNA metabarcoding, we detected prey species on 94.1% of the hawks sampled (n = 525/588) comprising 1396 prey species detections from 65 prey species. Prey frequency in diet and eBird relative abundance of prey species were correlated over the migration season for top-selected prey species, suggesting prey availability is an important component of raptor-songbird interactions during fall. Prey size, flocking behaviour and non-breeding habitat association were prey traits that significantly influenced predator choice. We found differences between female and male hawk prey selection, suggesting that sexual size dimorphism has led to distinct foraging strategies on migration. This research integrated field data collected by a volunteer-powered raptor migration monitoring station and public-generated data from eBird to reveal elusive predator-prey dynamics occurring in an ephemeral raptor-songbird community during fall migration. Understanding dynamic raptor-songbird interactions along migration routes remains a relatively unexplored frontier in animal ecology and is necessary for the conservation and management efforts of migratory and resident communities.

Durante la migración animal, las comunidades efímeras de taxones de todos los niveles tróficos coexisten en el espacio y el tiempo. Las interacciones entre depredadores y presas a lo largo de los corredores migratorios son significativas desde el punto de vista ecológica y evolutivo. Sin embargo, estas interacciones siguen siendo poco estudiadas en los sistemas terrestres y justifican más investigaciones utilizando enfoques novedosos. Investigamos las interacciones depredador­presa entre un depredador avívoro migratorio y una comunidad de presas aviares efímeras en la temporada migratoria otoñal. Probamos las asociaciones entre los rasgos de las aves y la selección de presas y planteamos la hipótesis de que los rasgos de las presas (tamaño relativo, comportamiento de bandada, hábitat, tendencia migratoria y disponibilidad) influirían en la selección de presas por parte de una rapaz sexualmente dimórfica durante la migración. Para documentar el consumo de presas, recogimos rastros de ADN de presas de picos y garras de Gavilán Americano Accipiter striatus (n = 588) migratorios. Determinamos la disponibilidad de presas en la comunidad de aves efímeras extrayendo índices de abundancia semanales de los datos de eBird Estado y Tendencias. Utilizamos modelos de elección discreta para evaluar la selección de presas y visualizamos la frecuencia de las presas en la dieta y la disponibilidad en el paisaje durante la temporada migratoria otoñal. Utilizando el metacódigo de barras del ADN ambiental, detectamos especies de presas en el 94,1% de los halcones muestreados (n = 525/588), comprendiendo 1396 detecciones de 65 especies de presas. La frecuencia de presas en la dieta y la abundancia relativa de especies de presas en eBird se correlacionaron a lo largo de la temporada de migración para las principales especies de presas seleccionadas, lo que sugiere que la disponibilidad de presas es un componente importante de las interacciones entre aves rapaces y aves canoras durante el otoño. El tamaño de las presas, el comportamiento de las bandadas y la asociación con el hábitat no reproductivo fueron rasgos de presa que influyeron significativamente en la elección de los depredadores. Encontramos diferencias entre la selección de presas de gavilán hembra y macho, lo que sugiere que el dimorfismo sexual de tamaño ha conducido a distintas estrategias de alimentación durante la migración. Esta investigación integró datos de campo recopilados por una estación de monitoreo de migración de rapaces impulsada por voluntarios y datos generados públicamente por eBird para revelar la esquiva dinámica depredador­presa que ocurre en una comunidad efímera de rapaces y aves canoras durante la migración otoñal. Comprender las interacciones dinámicas entre rapaces y aves canoras a lo largo de las rutas migratorias sigue siendo una frontera relativamente inexplorada en la ecología animal y es necesaria para los esfuerzos de conservación y gestión de las comunidades migratorias y residentes.

Evolutionarily stable payoff matrix in hawk-dove games.

BACKGROUND: Classical matrix game models aim to find the endpoint of behavioural evolution for a set of fixed possible interaction outcomes. Here, we introduce an evolutionary model in which not only the players' strategies but also the payoff matrix evolves according to natural selection. RESULTS: We start out from the hawk-dove matrix game and, in a way that is consistent with the monomorphic model setup of Maynard Smith and Price, introduce an evolving phenotypic trait that quantifies fighting ability and determines the probability of winning and the cost of losing escalated hawk-hawk fights. We define evolutionarily stable phenotypes as consisting of an evolutionarily stable strategy and an evolutionarily stable trait, which in turn describes a corresponding evolutionarily stable payoff matrix. CONCLUSIONS: We find that the maximal possible cost of escalating fights remains constant during evolution assuming a separation in the time scales of fast behavioural and slow trait selection, despite the fact that the final evolutionarily stable phenotype maximizes the payoff of hawk-hawk fights. Our results mirror the dual nature of Darwinian evolution whereby the criteria of evolutionary success, as well as the successful phenotypes themselves, are a product of natural selection.

Optimized operation strategy for energy storage charging piles based on multi-strategy hybrid improved Harris hawk algorithm.

In response to the issues arising from the disordered charging and discharging behavior of electric vehicle energy storage Charging piles, as well as the dynamic characteristics of electric vehicles, we have developed an ordered charging and discharging optimization scheduling strategy for energy storage Charging piles considering time-of-use electricity prices. The decision variables include the charging and discharging prices, states, and power of electric vehicles. We have constructed a mathematical model for electric vehicle charging and discharging scheduling with the optimization objectives of minimizing the charging and discharging costs of electric vehicles and maximizing the revenue of Charging piles. To address the challenges of multivariable, multi-objective, and high-dimensional optimization in the proposed model, we propose a Multi-strategy Hybrid Improved Harris Hawk Algorithm (MHIHHO). In addition, to validate the optimization performance of the proposed algorithm, CEC benchmark test functions are employed to assess the algorithm's optimization accuracy, convergence speed, stability, and significance. Finally, optimization-based scheduling simulations are performed considering power constraints for energy storage charging and discharging at different time intervals, as well as discharge loads. The proposed method reduces the peak-to-valley ratio of typical loads by 52.8 % compared to the original algorithm, effectively allocates charging piles to store electric power resources during off-peak periods, reduces user charging costs by 16.83 %-26.3 %, and increases Charging pile revenue.

An optimized framework for prediagnosis of diabetic retinopathy using HHO-CBL model.

BACKGROUND: Diabetic Retinopathy (DR) is a widespread intense stage of diabetes mellitus that causes vision-effecting anomalies in the retina. It is a medical health condition on the strength of fluctuating glucose level in the blood that can result in vision loss in case of severity. OBJECTIVE: As a result, early detection and treatment with DR is the most significant task which will tremendously reduce the likelihood of vision impairment and is still a difficult challenge. Many conventional methods fail to detect primary causes of formation of Microaneurysms, that are used to determine the Prediagnosis of DR. METHOD: To overcome this challenge, the proposed model incorporates Harris Hawk Optimization with CNN-Bi-LSTM (HHO-CBL) to extract the features. The Prediagnosis of DR has been achieved through this model by spotting saccular dilations, hyaline like material in the capillary aneurysm wall, kinking of vessels since these are the indications for the creation of microaneurysms that are spotted in the blood vessel of the retina. The recommended model is also used to automatically detect DR and its progression in many phases. Furthermore, in order to identify the severity of DR retina, we used a benchmark Kaggle APTOS dataset to train the HHO-CBL model. RESULTS: Experimental results reveal that this model obtains the best classification accuracy of 96.4 % for an early diagnosis and 98.8 % for a five-degree classification. In addition to those results, a comparison with previously carried out studies has also shown that this model provides a promising solution for a successful Prediagnosis of DR and its staging. CONCLUSIONS: In the current research, an innovative HHO-CBL was developed for identifying the primary causes that lead to the formation of microaneurysms and diagnosing all five grades of DR. According to the acquired results presented through the evaluation performance metrics indicates that the pre-early diagnosis and five grade classification using feature embedding technique outperformed the other prevailing approaches (Tab. 4, Fig. 10, Ref. 31).

Accessing populations of motor units.

A new device improves the way scientists can record the activity of motor units in a wide range of animals and settings.

Environmental Conditions and Vehicle Disturbance Influence Stress Behaviors in a Working Harris's Hawk (Parabuteo unicinctus).

Harris's hawk (Parabuteo unicinctus) is used for pest control, as their presence can deter wild birds such as gulls. Working Harris's hawk on UK waste sites is permitted in accordance with regulations and legislation. This study investigated the general environment of a waste site compound yard where a single Harris's hawk was flown for pest control. The hawk's behaviors were evaluated in an ethogram, alongside environmental measures, and disturbance levels. Data was analyzed using Generalised Linear Latent Variable Models (GLLVM) to elucidate the effects of disturbance and environment on hawk behaviors. Results suggested cloudy conditions encouraged grooming responses that were normal and relaxed in their nature. Rain, sun and wind conditions increased recognized stress behaviors. Frequency of disturbance by construction vehicles inside the compound increased stress behaviors, such that keepers are recommended to revise welfare conditions. Increased stress behaviors by birds worked in dynamic environments like waste recycling yards could potentially elicit damaging illness such as feather breaking behavior. Reducing stress factors for Harris's hawk in industrial working yards combined with amending husbandry practices will improve welfare for the species.

Epidermis lesion detection via optimized distributed capsule neural network.

Skin cancer, encompassing various forms such as melanoma, basal cell carcinoma, and others, remains a significant global health concern, often proving fatal if not diagnosed and treated in its early stages. The challenge of accurately diagnosing skin cancer, particularly melanoma, persists even for experienced dermatologists due to the intricate and unpredictable nature of its symptoms. To address the need for more accurate and efficient skin cancer detection, a novel Golden Hawk Optimization-based Distributed Capsule Neural Network (GHO-DCaNN) is proposed. This novel technique leverages advanced computational methods to improve the reliability and precision of skin cancer diagnosis. An optimized clustering-based segmentation approach is introduced, integrating the innovative Sewer Shad Fly Optimization (SSFO), which combines elements of both mayfly and moth flame optimization. This integration enhances the accuracy of lesion boundary delineation and feature extraction. The core of the innovation lies in the optimized distributed capsule neural network, which is trained using the Hybrid GHO. This optimizer, inspired by the behaviors of the golden eagle and fire hawk, ensures the effectiveness of epidermis lesion detection, pushing the boundaries of skin cancer diagnosis methods. The achievements based on the metrics, like specificity, sensitivity, and accuracy show 97.53%, 99.05%, and 98.83% for 90% of training and 97.83%, 99.50%, and 99.06% for k-fold of 10, respectively.

Myomatrix arrays for high-definition muscle recording.

Neurons coordinate their activity to produce an astonishing variety of motor behaviors. Our present understanding of motor control has grown rapidly thanks to new methods for recording and analyzing populations of many individual neurons over time. In contrast, current methods for recording the nervous system's actual motor output - the activation of muscle fibers by motor neurons - typically cannot detect the individual electrical events produced by muscle fibers during natural behaviors and scale poorly across species and muscle groups. Here we present a novel class of electrode devices ('Myomatrix arrays') that record muscle activity at unprecedented resolution across muscles and behaviors. High-density, flexible electrode arrays allow for stable recordings from the muscle fibers activated by a single motor neuron, called a 'motor unit,' during natural behaviors in many species, including mice, rats, primates, songbirds, frogs, and insects. This technology therefore allows the nervous system's motor output to be monitored in unprecedented detail during complex behaviors across species and muscle morphologies. We anticipate that this technology will allow rapid advances in understanding the neural control of behavior and identifying pathologies of the motor system.

A prospective approach to detect advanced persistent threats: Utilizing hybrid optimization technique.

Advanced Persistent Threat (APT) attacks pose significant challenges for AI models in detecting and mitigating sophisticated and highly effective cyber threats. This research introduces a novel concept called Hybrid HHOSSA which is the grouping of Harris Hawk Optimization (HHO) and Sparrow Search Algorithm (SSA) characteristics for optimizing the feature selection and data balancing in the context of APT detection. In addition, the light GBM as well as the weighted average Bi-LSTM are optimized by the proposed hybrid HHOSSA optimization. The HHOSSA-based attribute selection is used to choose the most important attributes from the provided dataset in the early step of the quasi-identifier detection. The HHOSSA-SMOTE algorithm effectively balances the unbalanced data, such as the lateral movements and the data exfiltration in the DAPT 2020 database, which further improves the classifier performance. The light GBM and the Bi-LSTM classifier hyperparameters are well attuned and classified by the HHOSSA optimization for the precise classification of the attacks. The outcome of both the optimized light GBM and the Bi-LSTM classifier generates the final prediction of the attacks existing in the network. According to the research findings, the HHOSSA-hybrid classifier achieves high accuracy in detecting attacks, with an accuracy rate of 94.468 %, a sensitivity of 94.650 %, and a specificity of 95.230 % with a K-fold value of 10. Also, the HHOSSA-hybrid classifier achieves the highest AUC percentage of 97.032, highlighting its exceptional performance in detecting APT attacks.

Use of tramadol as analgesic alternative in Harris hawk (Parabuteo unicinctus).

BACKGROUND: The Harris hawk is a bird of prey susceptible to traumatic injuries because it is useful for several purposes such as conservancy, biological control and falconry. Once received in rehabilitation centres or specialized clinics, it is necessary to provide proper analgesia. OBJECTIVES: The aim of this study is to demonstrate the analgesic efficacy of tramadol in Harris hawks (PISADOL 50 PiSA Agropecuaria, S.A. de C.V. Calle 1 Norte, Manzana 2-25 Parque Industrial Tula Atitalaquia, Hgo, México), by the assessment of nociceptive threshold. METHODS: A total of 24 adult Harris hawks were selected from a rehabilitation centre. The birds were randomly divided into four groups: control (saline solution), 5.0, 15.0 and 30.0 mg/kg of intramuscular tramadol. Nociception was produced with electrical stimuli of 9 V, applied in propatagial skin at 1, 5, 10, 20, 30, 45, 60, 90, 120, 180, 240, 300 and 360 min, assessing the nociceptive threshold and sedative effects produced by each treatment. RESULTS: No difference was observed between control and tramadol group 5 mg/kg. At 15 mg/kg, the pain threshold increased from 20 to 240 min, with minimal sedative effects. At 30 mg/kg, there was a marked increase in pain threshold from 10 to 300 min, and sedative effects like wing and head drooping for a period of 90 min. CONCLUSIONS: Tramadol can be an analgesic alternative for Harris's hawks, as it decreases the response to painful stimuli in this species when administered by intramuscular route.

A feature selection framework for anxiety disorder analysis using a novel multiview harris hawk optimization algorithm.

Machine learning (ML) has demonstrated its ability to exploit important relationships within data collection, which can be used in the diagnosis, treatment, and prediction of outcomes in a variety of clinical contexts. Anxiety mental disorder analysis is one of the pending difficulties that ML can help with. A thorough study is demanded to gain a better understanding of this illness. Since the anxiety data is generally multidimensional, which complicates processing and as a result of technology improvements, medical data from several perspectives, known as multiview data (MVD), is being collected. Each view has its own data type and feature values, so there is a lot of diversity. This work introduces a novel preprocessing feature selection (FS) approach, multiview harris hawk optimization (MHHO), which has the potential to reduce the dimensionality of anxiety data, hence reducing analytical effort. The uniqueness of MHHO originates from combining a multiview linking methodology with the power of the harris hawk optimization (HHO) method. The HHO is used to identify the lowest optimal MVD feature subset, while multiview linking is utilized to find a promising fitness function to direct the HHO FS while accounting for all data views' heterogeneity. The complexity of MHHO is O(THL2), where T is the number of iterations, H is the number of involved harris hawks, and L is the number of objects. Using two publicly available anxiety MVDs, MHHO is validated against ten recent rivals in its category. The experimental findings show that MHHO has a considerable advantage in terms of convergence speed (converging in less than ten iterations), subset size (removing 75% of the views; reducing feature size by 66%), and classification accuracy (approaching 100%). Furthermore, statistical analyses reveal that MHHO is statistically different from its competitors, bolstering its applicability. Finally, feature importance is evaluated, shedding light on the most anxiety-inducing characteristics. The likelihood of developing additional disorders (such as depression or stress) is also investigated.

Obstacle avoidance in aerial pursuit.

Pursuing prey through clutter is a complex and risky activity requiring integration of guidance subsystems for obstacle avoidance and target pursuit. The unobstructed pursuit trajectories of Harris' hawks Parabuteo unicinctus are well modeled by a mixed guidance law feeding back target deviation angle and line-of-sight rate. Here we ask how their pursuit behavior is modified in response to obstacles, using high-speed motion capture to reconstruct flight trajectories recorded during obstructed pursuit of maneuvering targets. We find that Harris' hawks use the same mixed guidance law during obstructed pursuit but appear to superpose a discrete bias command that resets their flight direction to aim at a clearance of approximately one wing length from an upcoming obstacle as they reach some threshold distance from it. Combining a feedback command in response to target motion with a feedforward command in response to upcoming obstacles provides an effective means of prioritizing obstacle avoidance while remaining locked-on to a target. We therefore anticipate that a similar mechanism may be used in terrestrial and aquatic pursuit. The same biased guidance law could also be used for obstacle avoidance in drones designed to intercept other drones in clutter, or to navigate between fixed waypoints in urban environments.