Diverse Targets Research Articles

Genistein: A promising phytoconstituent with reference to its bioactivities.

Genistein, a potent phytoconstituent, has garnered significant attention for its diverse bioactivities, making it a subject of extensive research and exploration. This review delves into the multifaceted properties of genistein, encompassing its antioxidant and anticancer potential. Its ability to modulate various cellular pathways and interact with diverse molecular targets has positioned it as a promising candidate in the prevention and treatment of various diseases. This review provides a comprehensive examination of Genistein, covering its chemical properties, methods of isolation, synthesis, therapeutic attributes with regard to cancer management, and the proposed mechanisms of action as put forth by researchers.

Enzymatic Routes for Chiral Amine Synthesis: Protein Engineering and Process Optimization.

Chiral amines are essential motifs in pharmaceuticals, agrochemicals, and specialty chemicals. While traditional chemical routes to chiral amines often lack stereoselectivity and require harsh conditions, biocatalytic methods using engineered enzymes can offer high efficiency and selectivity under sustainable conditions. This review discusses recent advances in protein engineering of transaminases, oxidases, and other enzymes to improve catalytic performance. Strategies such as directed evolution, immobilization, and computational redesign have expanded substrate scope and enhanced efficiency. Furthermore, process optimization guided by techno-economic assessments has been crucial for establishing viable biomanufacturing routes. Combining state-of-the-art enzyme engineering with multifaceted process development will enable scalable, economical enzymatic synthesis of diverse chiral amine targets.

Catalytic Activity of the Archetype from Group 4 of the FTR-like Ferredoxin:Thioredoxin Reductase Family Is Regulated by Unique S = 7/2 and S = 1/2 [4Fe-4S] Clusters.

Thioredoxin reductases (TrxR) activate thioredoxins (Trx) that regulate the activity of diverse target proteins essential to prokaryotic and eukaryotic life. However, very little is understood of TrxR/Trx systems and redox control in methanogenic microbes from the domain Archaea (methanogens), for which genomes are abundant with annotations for ferredoxin:thioredoxin reductases [Fdx/thioredoxin reductase (FTR)] from group 4 of the widespread FTR-like family. Only two from the FTR-like family are characterized: the plant-type FTR from group 1 and FDR from group 6. Herein, the group 4 archetype (AFTR) from Methanosarcina acetivorans was characterized to advance understanding of the family and TrxR/Trx systems in methanogens. The modeled structure of AFTR, together with EPR and Mössbauer spectroscopies, supports a catalytic mechanism similar to plant-type FTR and FDR, albeit with important exceptions. EPR spectroscopy of reduced AFTR identified a transient [4Fe-4S]1+ cluster exhibiting a mixture of S = 7/2 and typical S = 1/2 signals, although rare for proteins containing [4Fe-4S] clusters, it is most likely the on-pathway intermediate in the disulfide reduction. Furthermore, an active site histidine equivalent to residues essential for the activity of plant-type FTR and FDR was found dispensable for AFTR. Finally, a unique thioredoxin system was reconstituted from AFTR, ferredoxin, and Trx2 from M. acetivorans, for which specialized target proteins were identified that are essential for growth and other diverse metabolisms.

Neuronal Subtypes and Connectivity of the Adult Mouse Paralaminar Amygdala.

The paralaminar nucleus of the amygdala (PL) comprises neurons that exhibit delayed maturation. PL neurons are born during gestation but mature during adolescent ages, differentiating into excitatory neurons. These late-maturing PL neurons contribute to the increase in size and cell number of the amygdala between birth and adulthood. However, the function of the PL upon maturation is unknown, as the region has only recently begun to be characterized in detail. In this study, we investigated key defining features of the adult mouse PL; the intrinsic morpho-electric properties of its neurons, and its input and output circuit connectivity. We identify two subtypes of excitatory neurons in the PL based on unsupervised clustering of electrophysiological properties. These subtypes are defined by differential action potential firing properties and dendritic architecture, suggesting divergent functional roles. We further uncover major axonal inputs to the adult PL from the main olfactory network and basolateral amygdala. We also find that axonal outputs from the PL project reciprocally to these inputs and to diverse targets including the amygdala, frontal cortex, hippocampus, hypothalamus, and brainstem. Thus, the adult mouse PL is centrally placed to play a major role in the integration of olfactory sensory information, to coordinate affective and autonomic behavioral responses to salient odor stimuli.

1,3,5-Triazine as Branching Connector for the Construction of Novel Antimicrobial Peptide Dendrimers: Synthesis and Biological Characterization.

Peptides displaying antimicrobial properties are being regarded as useful tools to evade and combat antimicrobial resistance, a major public health challenge. Here we have addressed dendrimers, attractive molecules in pharmaceutical innovation and development displaying broad biological activity. Triazine-based dendrimers were fully synthesized in the solid phase, and their antimicrobial activity and some insights into their mechanisms of action were explored. Triazine is present in a large number of compounds with highly diverse biological targets with broad biological activities and could be an excellent branching unit to accommodate peptides. Our results show that the novel peptide dendrimers synthesized have remarkable antimicrobial activity against Gram-negative bacteria (E. coli and P. aeruginosa) and suggest that they may be useful in neutralizing the effect of efflux machinery on resistance.

Impact of metformin on melanoma: a meta-analysis and systematic review

BackgroundThere is evidence of a modest reduction in skin cancer risk among metformin users. However, no studies have further examined the effects of metformin on melanoma survival and safety outcomes. This study aimed to quantitatively summarize any influence of metformin on the overall survival (OS) and immune-related adverse effects (irAEs) in melanoma patients.MethodsSelection criteria: The inclusion criteria were designed based on the PICOS principles. Information sources: PubMed, EMBASE, Cochrane Library, and Web of Science were searched for relevant literature published from the inception of these databases until November 2023 using ‘Melanoma’ and ‘Metformin’ as keywords. Survival outcomes were OS, progression-free survival (PFS), recurrence-free survival (RFS), and mortality; the safety outcome was irAEs. Risk of bias and data Synthesis: The Cochrane tool for assessing the risk of bias in randomized trial 2 (RoB2) and methodological index for non-randomized studies (MINORS) were selected to assess the risk of bias. The Cochrane Q and I2 statistics based on Stata 15.1 SE were used to test the heterogeneity among all studies. Funnel plot, Egger regression, and Begg tests were used to evaluate publication bias. The leave-one-out method was selected as the sensitivity analysis tool.ResultsA total of 12 studies were included, involving 111,036 melanoma patients. The pooled HR for OS was 0.64 (95% CI [0.42, 1.00], p = 0.004, I2 = 73.7%), HR for PFS was 0.89 (95% CI [0.70, 1.12], p = 0.163, I2 = 41.4%), HR for RFS was 0.62 (95% CI [0.26, 1.48], p = 0.085, I2 = 66.3%), and HR for mortality was 0.53 (95% CI [0.46, 0.63], p = 0.775, I2 = 0.0%). There was no significant difference in irAEs incidence (OR = 1.01; 95% CI [0.42, 2.41]; p = 0.642) between metformin and no metformin groups.DiscussionThe improvement in overall survival of melanoma patients with metformin may indirectly result from its diverse biological targets and beneficial effects on multiple systemic diseases. While we could not demonstrate a specific improvement in the survival of melanoma patients, the combined benefits and safety of metformin for patients taking the drug are worthy of recognition.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42024518182.

Reversibly Reactive Affinity Selection-Mass Spectrometry Enables Identification of Covalent Peptide Binders.

Covalent peptide binders have found applications as activity-based probes and as irreversible therapeutic inhibitors. Currently, there is no rapid, label-free, and tunable affinity selection platform to enrich covalent reactive peptide binders from synthetic libraries. We address this challenge by developing a reversibly reactive affinity selection platform termed ReAct-ASMS enabled by tandem high-resolution mass spectrometry (MS/MS) to identify covalent peptide binders to native protein targets. It uses mixed disulfide-containing peptides to build reversible peptide-protein conjugates that can enrich for covalent variants, which can be sequenced by MS/MS after reduction. Using this platform, we identified covalent peptide binders against two oncoproteins, human papillomavirus 16 early protein 6 (HPV16 E6) and peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 protein (Pin1). The resulting peptide binders efficiently and selectively cross-link Cys58 of E6 at 37 °C and Cys113 of Pin1 at room temperature, respectively. ReAct-ASMS enables the identification of highly selective covalent peptide binders for diverse molecular targets, introducing an applicable platform to assist preclinical therapeutic development pipelines.

Socio-environmental Opportunities for Organic Material Management in California's Sustainability Transition.

Contemporary resource management is doubly burdened by high rates of organic material disposal in landfills, generating potent greenhouse gases (GHG), and globally degraded soils, which threaten future food security. Expansion of composting can provide a resilient alternative, by avoiding landfill GHG emissions, returning valuable nutrients to the soil to ensure continued agricultural production, and sequestering carbon while supporting local communities. Recognizing this opportunity, California has set ambitious organics diversion targets in the Short-Lived Climate Pollutant Law (SB1383) which will require significant increases (5 to 8 million tonnes per year) in organic material processing capacity. This paper develops a spatial optimization model to consider how to handle this flow of additional material while achieving myriad social and ecological benefits through compost production. We consider community-based and on-farm facilities alongside centralized, large-scale infrastructure to explore decentralized and diversified alternative futures of composting infrastructure in the state of California. We find using a diversity of facilities would provide opportunity for cost savings while achieving significant emissions reductions of approximately 3.4 ± 1 MMT CO2e and demonstrate that it is possible to incorporate community protection into compost infrastructure planning while meeting economic and environmental objectives.

Fine-Grained Feature Perception for Unmanned Aerial Vehicle Target Detection Algorithm

Unmanned aerial vehicle (UAV) aerial images often present challenges such as small target sizes, high target density, varied shooting angles, and dynamic poses. Existing target detection algorithms exhibit a noticeable performance decline when confronted with UAV aerial images compared to general scenes. This paper proposes an outstanding small target detection algorithm for UAVs, named Fine-Grained Feature Perception YOLOv8s-P2 (FGFP-YOLOv8s-P2), based on YOLOv8s-P2 architecture. We specialize in improving inspection accuracy while meeting real-time inspection requirements. First, we enhance the targets’ pixel information by utilizing slice-assisted training and inference techniques, thereby reducing missed detections. Then, we propose a feature extraction module with deformable convolutions. Decoupling the learning process of offset and modulation scalar enables better adaptation to variations in the size and shape of diverse targets. In addition, we introduce a large kernel spatial pyramid pooling module. By cascading convolutions, we leverage the advantages of large kernels to flexibly adjust the model’s attention to various regions of high-level feature maps, better adapting to complex visual scenes and circumventing the cost drawbacks associated with large kernels. To match the excellent real-time detection performance of the baseline model, we propose an improved Random FasterNet Block. This block introduces randomness during convolution and captures spatial features of non-linear transformation channels, enriching feature representations and enhancing model efficiency. Extensive experiments and comprehensive evaluations on the VisDrone2019 and DOTA-v1.0 datasets demonstrate the effectiveness of FGFP-YOLOv8s-P2. This achievement provides robust technical support for efficient small target detection by UAVs in complex scenarios.

Impact of Duchenne and non-Duchenne smiles on perceived trustworthiness of Black and White faces: A Black perspective.

In five experiments, we investigated how Black participants perceive Duchenne and non-Duchenne smiles on Black and White targets. Results consistently demonstrated that when assessing happiness, faces with Duchenne compared to non-Duchenne smiles were rated as happier on both Black and White targets. However, when assessing a more socially evaluative dimension, trustworthiness, perceptions of Black and White targets diverged. Whereas White targets with Duchenne compared to non-Duchenne smiles were rated as more trustworthy, ratings of Black targets with Duchenne and non-Duchenne smiles did not differ, with both appraised as highly trustworthy. Although the degree to which Black participants identified with their race did not moderate these effects, the perceived genuineness of targets did mediate the relationship. One reason why Duchenne compared to non-Duchenne smiles on White but not Black targets were perceived as more trustworthy is because Duchenne compared to non-Duchenne smiles on White but not Black targets were perceived as more genuine. A final study extended these findings by exploring the impact of target race and smile type on partner choice. In accordance with the results related to trustworthiness ratings, Black participants selected White partners with Duchenne compared to non-Duchenne smiles more often but did not differentiate in their choice of Black partners with Duchenne versus non-Duchenne smiles. These findings underscore the importance of investigating not only diverse targets but also diverse perceivers. Our results suggest that Black perceivers use facial cues differently when rating the trustworthiness of Black and White targets and that these perceptions have important downstream consequences. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Streamlining Computational Fragment-Based Drug Discovery through Evolutionary Optimization Informed by Ligand-Based Virtual Prescreening.

Recent advances in computational methods provide the promise of dramatically accelerating drug discovery. While mathematical modeling and machine learning have become vital in predicting drug-target interactions and properties, there is untapped potential in computational drug discovery due to the vast and complex chemical space. This paper builds on our recently published computational fragment-based drug discovery (FBDD) method called fragment databases from screened ligand drug discovery (FDSL-DD). FDSL-DD uses in silico screening to identify ligands from a vast library, fragmenting them while attaching specific attributes based on predicted binding affinity and interaction with the target subdomain. In this paper, we further propose a two-stage optimization method that utilizes the information from prescreening to optimize computational ligand synthesis. We hypothesize that using prescreening information for optimization shrinks the search space and focuses on promising regions, thereby improving the optimization for candidate ligands. The first optimization stage assembles these fragments into larger compounds using genetic algorithms, followed by a second stage of iterative refinement to produce compounds with enhanced bioactivity. To demonstrate broad applicability, the methodology is demonstrated on three diverse protein targets found in human solid cancers, bacterial antimicrobial resistance, and the SARS-CoV-2 virus. Combined, the proposed FDSL-DD and a two-stage optimization approach yield high-affinity ligand candidates more efficiently than other state-of-the-art computational FBDD methods. We further show that a multiobjective optimization method accounting for drug-likeness can still produce potential candidate ligands with a high binding affinity. Overall, the results demonstrate that integrating detailed chemical information with a constrained search framework can markedly optimize the initial drug discovery process, offering a more precise and efficient route to developing new therapeutics.

Exploring the optimal lower blood pressure boundary during endovascular thrombectomy in patients with large vessel occlusion

BackgroundCurrent guidelines advocate for maintaining BP level below 180/105 mmHg during EVT, determining the safe lower boundary remains primarily consensus-driven by experts. This study aims to delve into the correlation between various targets of lower boundary for systolic and diastolic BP (SBP and DBP) during EVT and 3-month functional outcomes. MethodsA cohort study was conducted across two EVT-capable centers, enrolling patients with large artery occlusion undergoing EVT within 8 h of stroke onset. Mean BP values during EVT were meticulously recorded, and logistic regression models were utilized to evaluate the correlation between outcomes and diverse lower boundary targets for SBP and DBP. Additionally, logistic regression models investigated the relationship between periprocedural BP variability and subsequent outcomes. ResultsAmong the 201 patients included, having a SBP higher than 130 or 140 mmHg showed an independent association with increased good functional outcomes at 3 months (adjusted odds ratio, aOR 2.80, 95% Cis, 1.26–6.39 for 140 mmHg; aOR 2.34, 95% Cis, 1.03–5.56 for 130 mmHg). Additionally, an SBP exceeding 130 mmHg was correlated with decreased 3-month mortality (aOR, 0.24, 95% CI 0.07–0.74). No significant relationship was observed between DBP and functional outcomes. Patients with higher periprocedural SBP coefficient variance exhibited a decreased rate of good functional outcomes at 3 months (aOR, 0.42, 95% CI, 0.18–0.96). ConclusionsA SBP range above 130–140 mmHg could potentially serve as a safe lower boundary during EVT, while minimizing BP fluctuations may correlate with improved post-EVT functional outcomes.

Emerging trends in functional molecularly imprinted polymers for electrochemical detection of biomarkers

Molecularly imprinted polymers (MIPs), functioning as artificial bioreceptors, hold significant promise for biomarker detection in healthcare, disease diagnosis, and addressing drug abuse. In contrast to natural bioreceptors, MIP-based sensors offer numerous advantages, such as high stability, cost-effectiveness, high selectivity, sensitivity, and notably straightforward preparation with customizable binding sites for diverse targets. Conventional MIP sensors often necessitate external redox reagents in analytes to transduce binding events into electrochemical signals for indirect detection, presenting challenges for practical applications in wearables or point-of-care (POC) testing. Redox-active MIP sensors have emerged as a viable alternative, enabling direct and label-free electrochemical detection, with two types developed. The first type utilizes electrocatalytic materials to expedite electron transfer and facilitate a redox reaction between the captured electroactive target and the electrode. The second type incorporates an embedded redox reactive component that allows selective binding of a target to modulate its electron transfer, leading to a change in the electrical signal. This review covers emerging trends and challenges in redox-active MIP sensors for direct electrochemical detection of biomarkers, focusing on sensing mechanisms, synthesis methods, and applications. Additionally, recent progress in wearable and POC redox-active MIP sensors is highlighted. A comprehensive outlook of challenges is further provided, aiming to advance direct biomarker detection for diverse healthcare applications.

An AI-Based Approach for Automating Penetration Testing

Abstract: Cyber penetration testing (pen-testing) is important in revealing possible weaknesses and breaches in network systems that can ultimately help in curbing cybercrimes. Nevertheless, even with the current drive to mechanize pen- testing, there are still a number of challenges which include incomplete frameworks and low precision in automation methods. This paper aims at addressing them by suggesting hybrid AI-based automation framework specifically for Pen- Testing through integration of smart algorithms and automated tools. As indicated by recent studies, it goes further into proposing a holistic approach towards maximizing the efficiency as well as effectiveness of Pen-Testing processes. Furthermore, it also identifies the need for machine learning techniques such as reinforcement learning and deep reinforcement learning for automating Pen-Testing activities. MITRE ATT&CK Framework being utilized within the proposed model imitates real-life cyber-attacks and exploits hence facilitating automated testing across diverse target networks. Based on comparison with manual penetration testing reports, this study reviews how effective the new automated method is when compared to old ways used in manual penetration tests while providing some direction for future developments along with suggestions in the field of self- governing intrusion detection systems (IDS)

The impact of singing on estrogen and progesterone release among female

Scientific studies have suggested that singing can have positive effect on people’s health. (NAFME, 2012). John Daniel Scott, has cited that people who sing are more likely to be happy because singing elevates the level of neurotransmitters which is associated with pleasure and wellbeing. Most studies on the biological effect of music has resolved around studies involving brain mapping and physiological parameters such as heart rate, blood pressure, through production of messengers such as hormones, cytokines and proteins, music elicit biological responses to stress, emotions and immune functions. Hormone certainly prove necessary for proper physiological growth and development. This study is therefore to know the influence of singing on female sex hormone release which has variety of effects on both sexual organs and diverse target tissues. This research study will be a cross sectional and controlled investigative study. Hormone will be assayed with ELIZA technique and its secretion will be determined by its concentration in the blood. The study will be carried out with a total of thirty female choral singers, grouped into three A, B, C, each group will comprise of ten subjects, using group C as control. Blood sample will collect from each group at pre and post ovulation phase of their menstrual cycle, before and after singing while the control group will not be involved in singing. The mean serum concentration in all group will be compared with group C which serve as control group.

Comprehensive Review on the Virulence Factors and Therapeutic Strategies with the Aid of Artificial Intelligence against Mucormycosis.

Mucormycosis, a rare but deadly fungal infection, was an epidemic during the COVID-19 pandemic. The rise in cases (COVID-19-associated mucormycosis, CAM) is attributed to excessive steroid and antibiotic use, poor hospital hygiene, and crowded settings. Major contributing factors include diabetes and weakened immune systems. The main manifesting forms of CAM─cutaneous, pulmonary, and the deadliest, rhinocerebral─and disseminated infections elevated mortality rates to 85%. Recent focus lies on small-molecule inhibitors due to their advantages over standard treatments like surgery and liposomal amphotericin B (which carry several long-term adverse effects), offering potential central nervous system penetration, diverse targets, and simpler dosing owing to their small size, rendering the ability to traverse the blood-brain barrier via passive diffusion facilitated by the phospholipid membrane. Adaptation and versatility in mucormycosis are facilitated by a multitude of virulence factors, enabling the pathogen to dynamically respond to various environmental stressors. A comprehensive understanding of these virulence mechanisms is imperative for devising effective therapeutic interventions against this highly opportunistic pathogen that thrives in immunocompromised individuals through its angio-invasive nature. Hence, this Review delineates the principal virulence factors of mucormycosis, the mechanisms it employs to persist in challenging host environments, and the current progress in developing small-molecule inhibitors against them.

TFITrack: Transformer Feature Integration Network for Object Tracking

AbstractDue to the ignoring of rich spatio-temporal and global contextual information with convolutional neural networks in features extraction, the traditional method is prone to tracking drift or even failure in complex scenario, especially for the tiny targets in aerial photography scenario. In this work, it proposes a transformer feature integration network (TFITrack) to obtain diverse and comprehensive target feature for the robust object tracking. Based on the typical transformer architecture, it optimizes encoder and decoder structure for aggregating discriminative spatio-temporal information and global context-awareness feature. Furthermore, the encoder introduces the similarity calculation layer and dual-attention module; the aim is to deepen the similarity between features and make corrections for channel and spatial dimensions, and feature representation is improved. Finally, with the introduction of the temporal context filtering layer, unimportant feature information is ignored adaptively, obtaining a balance between the parameters number reduction and stable performance. Experimental results show that the proposed tracking algorithm exhibits excellent tracking performance on seven benchmark datasets, especially on the aerial dataset UAV123, UAV20L, and UAV123@10fps, which presents the advantages of the novel method in dealing with fast motion and external interference.

Cell Painting-based bioactivity prediction boosts high-throughput screening hit-rates and compound diversity

Identifying active compounds for a target is a time- and resource-intensive task in early drug discovery. Accurate bioactivity prediction using morphological profiles could streamline the process, enabling smaller, more focused compound screens. We investigate the potential of deep learning on unrefined single-concentration activity readouts and Cell Painting data, to predict compound activity across 140 diverse assays. We observe an average ROC-AUC of 0.744 ± 0.108 with 62% of assays achieving ≥0.7, 30% ≥0.8, and 7% ≥0.9. In many cases, the high prediction performance can be achieved using only brightfield images instead of multichannel fluorescence images. A comprehensive analysis shows that Cell Painting-based bioactivity prediction is robust across assay types, technologies, and target classes, with cell-based assays and kinase targets being particularly well-suited for prediction. Experimental validation confirms the enrichment of active compounds. Our findings indicate that models trained on Cell Painting data, combined with a small set of single-concentration data points, can reliably predict the activity of a compound library across diverse targets and assays while maintaining high hit rates and scaffold diversity. This approach has the potential to reduce the size of screening campaigns, saving time and resources, and enabling primary screening with more complex assays.

Chemical Genetics in C. elegans Identifies Anticancer Mycotoxins Chaetocin and Chetomin as Potent Inducers of a Nuclear Metal Homeostasis Response.

C. elegans numr-1/2 (nuclear-localized metal-responsive) is an identical gene pair encoding a nuclear protein previously shown to be activated by cadmium and disruption of the integrator RNA metabolism complex. We took a chemical genetic approach to further characterize regulation of this novel metal response by screening 41,716 compounds and extracts for numr-1p::GFP activation. The most potent activator was chaetocin, a fungal 3,6-epidithiodiketopiperazine (ETP) with promising anticancer activity. Chaetocin activates numr-1/2 strongly in the alimentary canal but is distinct from metal exposure, because it represses canonical cadmium-responsive metallothionine genes. Chaetocin has diverse targets in cancer cells including thioredoxin reductase, histone lysine methyltransferase, and acetyltransferase p300/CBP; further work is needed to identify the mechanism in C. elegans as genetic disruption and RNAi screening of homologues did not induce numr-1/2 in the alimentary canal and chaetocin did not affect markers of integrator dysfunction. We demonstrate that disulfides in chaetocin and chetomin, a dimeric ETP analog, are required to induce numr-1/2. ETP monomer gliotoxin, despite possessing a disulfide linkage, had almost no effect on numr-1/2, suggesting a dimer requirement. Chetomin inhibits C. elegans growth at low micromolar levels, and loss of numr-1/2 increases sensitivity; C. elegans and Chaetomiaceae fungi inhabit similar environments raising the possibility that numr-1/2 functions as a defense mechanism. There is no direct orthologue of numr-1/2 in humans, but RNaseq suggests that chaetocin affects expression of cellular processes linked to stress response and metal homeostasis in colorectal cancer cells. Our results reveal interactions between metal response gene regulation and ETPs and identify a potential mechanism of resistance to this versatile class of preclinical compounds.

Collaborative target assignment problem for large-scale UAV swarm based on two-stage greedy auction algorithm

This paper introduces a collaborative allocation model designed for multiple UAVs and diverse targets in maritime combat situations. The model incorporates factors such as distance, angle, interception rate, and recognition rate to comprehensively represent the UAVs' overall damage advantage against targets. Given the complexity of real-world environments and real-time demands, large-scale UAV swarm missions necessitate swift and effective responses. To address this, the paper proposes a Two-Stage Greedy Auction Algorithm, enabling the rapid and efficient completion of cooperative strike tasks within large-scale UAV swarms while preventing deadlock occurrences. In the initial allocation stage, the entropy weight method is utilized to assess task advantages, ensuring a rational allocation criterion for various metrics during the strike process. Subsequently, to enhance the overall effective strike rate within all constraints, a reassignment algorithm is designed based on effective strike benefit indices and the initial assignment result. Simulation results demonstrate the algorithm's quick and stable running time in small-scale and large-scale scenarios.