Seasonal increase in nest defense,but not egg rejection,in a cuckoo host

The interactions between avian brood parasites and their hosts provide an informative and easy-to-handle system for studying coevolution.Avian brood parasitism reduces the reproductive success of hosts,and thus,hosts have evolved anti-parasitic strategies,such as rejecting parasitic eggs and adopting aggressive nest defense strategies,to avoid the cost brought on by brood parasitism.To test whether host anti-parasitic strategies are adjusted with the risk of being parasitized when the breeding seasons of brood parasites and hosts are not synchronous,we conducted a field experiment assessing nest defense and egg recognition behaviors of the Isabelline Shrike(Lanius isabellinus),a host of the Common Cuckoo(Cuculus canorus).In the local area,the host Isabelline Shrike begins to breed in April,whereas the summer migratory Common Cuckoo migrates to the local area in May and begins to lay parasitic eggs.Results showed that nest defense behaviors of the Isabelline Shrike increases significantly after cuckoo arrival,showing higher aggressiveness to cuckoo dummies,with no significant difference in attack rates among cuckoo,sparrowhawk and dove dummies,but their egg rejection did not change significantly.These results imply that Isabelline Shrikes may adjust their nest defense behavior,but not egg rejection behavior,with seasonality.

An Active Deception Defense Model Based on Address Mutation and Fingerprint Camouflage

The static and predictable characteristics of cyber systems give attackers an asymmetric advantage in gathering useful information and launching attacks.To reverse this asymmetric advantage,a new defense idea,called Moving Target Defense(MTD),has been proposed to provide additional selectable measures to complement traditional defense.However,MTD is unable to defeat the sophisticated attacker with fingerprint tracking ability.To overcome this limitation,we go one step beyond and show that the combination of MTD and Deception-based Cyber Defense(DCD)can achieve higher performance than either of them.In particular,we first introduce and formalize a novel attacker model named Scan and Foothold Attack(SFA)based on cyber kill chain.Afterwards,we develop probabilistic models for SFA defenses to provide a deeper analysis of the theoretical effect under different defense strategies.These models quantify attack success probability and the probability that the attacker will be deceived under various conditions,such as the size of address space,and the number of hosts,attack analysis time.Finally,the experimental results show that the actual defense effect of each strategy almost perfectly follows its probabilistic model.Also,the defense strategy of combining address mutation and fingerprint camouflage can achieve a better defense effect than the single address mutation.

Starlet:Network defense resource allocation with multi-armed bandits for cloud-edge crowd sensing in IoT

The cloud platform has limited defense resources to fully protect the edge servers used to process crowd sensing data in Internet of Things.To guarantee the network's overall security,we present a network defense resource allocation with multi-armed bandits to maximize the network's overall benefit.Firstly,we propose the method for dynamic setting of node defense resource thresholds to obtain the defender(attacker)benefit function of edge servers(nodes)and distribution.Secondly,we design a defense resource sharing mechanism for neighboring nodes to obtain the defense capability of nodes.Subsequently,we use the decomposability and Lipschitz conti-nuity of the defender's total expected utility to reduce the difference between the utility's discrete and continuous arms and analyze the difference theoretically.Finally,experimental results show that the method maximizes the defender's total expected utility and reduces the difference between the discrete and continuous arms of the utility.

A Function-Aware Mimic Defense Theory and Its Practice

In recent years,network attacks have been characterized by diversification and scale,which indicates a requirement for defense strategies to sacrifice generalizability for higher security.As the latest theoretical achievement in active defense,mimic defense demonstrates high robustness against complex attacks.This study proposes a Function-aware,Bayesian adjudication,and Adaptive updating Mimic Defense(FBAMD)theory for addressing the current problems of existing work including limited ability to resist unknown threats,imprecise heterogeneous metrics,and over-reliance on relatively-correct axiom.FBAMD incorporates three critical steps.Firstly,the common features of executors’vulnerabilities are obtained from the perspective of the functional implementation(i.e,input-output relationships extraction).Secondly,a new adjudication mechanism considering Bayes’theory is proposed by leveraging the advantages of both current results and historical confidence.Furthermore,posterior confidence can be updated regularly with prior adjudication information,which provides mimic system adaptability.The experimental analysis shows that FBAMD exhibits the best performance in the face of different types of attacks compared to the state-of-the-art over real-world datasets.This study presents a promising step toward the theo-retical innovation of mimic defense.

Privacy-Preserving Large-Scale AI Models for Intelligent Railway Transportation Systems:Hierarchical Poisoning Attacks and Defenses in Federated Learning

The development of Intelligent Railway Transportation Systems necessitates incorporating privacy-preserving mechanisms into AI models to protect sensitive information and enhance system efficiency.Federated learning offers a promising solution by allowing multiple clients to train models collaboratively without sharing private data.However,despite its privacy benefits,federated learning systems are vulnerable to poisoning attacks,where adversaries alter local model parameters on compromised clients and send malicious updates to the server,potentially compromising the global model’s accuracy.In this study,we introduce PMM(Perturbation coefficient Multiplied by Maximum value),a new poisoning attack method that perturbs model updates layer by layer,demonstrating the threat of poisoning attacks faced by federated learning.Extensive experiments across three distinct datasets have demonstrated PMM’s ability to significantly reduce the global model’s accuracy.Additionally,we propose an effective defense method,namely CLBL(Cluster Layer By Layer).Experiment results on three datasets have confirmed CLBL’s effectiveness.

Plant Chemical Defenses against Insect Herbivores—Using theWild Tobacco as a Model

The Nicotiana genus, commonly known as tobacco, holds significant importance as a crucial economic crop. Confrontedwith an abundance of herbivorous insects that pose a substantial threat to yield, tobacco has developed adiverse and sophisticated array of mechanisms, establishing itself as a model of plant ecological defense. Thisreview provides a concise overview of the current understanding of tobacco’s defense strategies against herbivores.Direct defenses, exemplified by its well-known tactic of secreting the alkaloid nicotine, serve as a potent toxinagainst a broad spectrum of herbivorous pests. Moreover, in response to herbivore attacks, tobacco enhancesthe discharge of volatile compounds, harnessing an indirect strategy that attracts the predators of the herbivores.The delicate balance between defense and growth leads to the initiation of most defense strategies only after aherbivore attack. Among plant hormones, notably jasmonic acid (JA), play central roles in coordinating thesedefense processes. JA signaling interacts with other plant hormone signaling pathways to facilitate the extensivetranscriptional and metabolic adjustments in plants following herbivore assault. By shedding light on these ecologicaldefense strategies, this review emphasizes not only tobacco’s remarkable adaptability in its natural habitatbut also offers insights beneficial for enhancing the resilience of current crops.

A shield of defense:Developing ballistic composite panels with effective electromagnetic interference shielding absorption

The primary goal of this study is to develop cost-effective shield materials that offer effective protection against high-velocity ballistic impact and electromagnetic interference(EMI)shielding capabilities through absorption.Six fiber-reinforced epoxy composite panels,each with a different fabric material and stacking sequence,have been fabricated using a hand-layup vacuum bagging process.Two panels made of Kevlar and glass fibers,referred to as(K-NIJ)and(G-NIJ),have been tested according to the National Institute of Justice ballistic resistance protective materials test NIJ 0108.01 Standard-Level IIIA(9 mm×19 mm FMJ 124 g)test.Three panels,namely,a hybrid of Kevlar and glass(H-S),glass with ceramic particles(C-S),and glass with recycled rubber(R-S)have been impacted by the bullet at the center,while the fourth panel made of glass fiber(G-S)has been impacted at the side.EMI shielding properties have been measured in the X-band frequency range via the reflection-transmission method.Results indicate that four panels(K-NIJ,G-NIJ,H-S,and G-S)are capable of withstanding high-velocity impact by stopping the bullet from penetrating through the panels while maintaining their structural integrity.However,under such conditions,these panels may experience localized delamination with variable severity.The EMI measurements reveal that the highest absorptivity observed is 88% for the KNIJ panel at 10.8 GHz,while all panels maintain an average absorptivity above 65%.All panels act as a lossy medium with a peak absorptivity at different frequencies,with K-NIJ and H-S panels demonstrating the highest absorptivity.In summary,the study results in the development of a novel,costeffective,multifunctional glass fiber epoxy composite that combines ballistic and electromagnetic interference shielding properties.The material has been developed using a simple manufacturing method and exhibits remarkable ballistic protection that outperforms Kevlar in terms of shielding efficiency;no bullet penetration or back face signature is observed,and it also demonstrates high EMI shielding absorption.Overall,the materials developed show great promise for various applications,including the military and defense.

Adversarial Defense Technology for Small Infrared Targets

With the rapid development of deep learning-based detection algorithms,deep learning is widely used in the field of infrared small target detection.However,well-designed adversarial samples can fool human visual perception,directly causing a serious decline in the detection quality of the recognition model.In this paper,an adversarial defense technology for small infrared targets is proposed to improve model robustness.The adversarial samples with strong migration can not only improve the generalization of defense technology,but also save the training cost.Therefore,this study adopts the concept of maximizing multidimensional feature distortion,applying noise to clean samples to serve as subsequent training samples.On this basis,this study proposes an inverse perturbation elimination method based on Generative Adversarial Networks(GAN)to realize the adversarial defense,and design the generator and discriminator for infrared small targets,aiming to make both of them compete with each other to continuously improve the performance of the model,find out the commonalities and differences between the adversarial samples and the original samples.Through experimental verification,our defense algorithm is not only able to cope with multiple attacks but also performs well on different recognition models compared to commonly used defense algorithms,making it a plug-and-play efficient adversarial defense technique.

Two intestinal microbiota-derived metabolites, deoxycholic acid and butyrate, synergize to enhance host defense peptide synthesis and alleviate necrotic enteritis

Background Necrotic enteritis(NE)is a major enteric disease in poultry,yet effective mitigation strategies remain elusive.Deoxycholic acid(DCA)and butyrate,two major metabolites derived from the intestinal microbiota,have independently been shown to induce host defense peptide(HDP)synthesis.However,the potential synergy between these two compounds remains unexplored.Methods To investigate the possible synergistic effect between DCA and butyrate in regulating HDP synthesis and barrier function,we treated chicken HD11 macrophage cells and jejunal explants with DCA and sodium butyrate(NaB),either individually or in combination,for 24 h.Subsequently,we performed RNA isolation and reverse transcrip-tion-quantitative PCR to analyze HDP genes as well as the major genes associated with barrier function.To further determine the synergy between DCA and NaB in enhancing NE resistance,we conducted two independent trials with Cobb broiler chicks.In each trial,the diet was supplemented with DCA or NaB on the day-of-hatch,followed by NE induction through sequential challenges with Eimeria maxima and Clostridium perfringens on d 10 and 14,respectively.We recorded animal mortality after infection and assessed intestinal lesions on d 17.The impact of DCA and NaB on the microbiota in the ileum and cecum was evaluated through bacterial 16S rRNA gene sequencing.Results We found that the combination of DCA and NaB synergistically induced multiple HDP genes in both chicken HD11 cells and jejunal explants.Additionally,the gene for claudin-1,a major tight junction protein,also exhibited synergistic induction in response to DCA and NaB.Furthermore,dietary supplementation with a combination of 0.75 g/kg DCA and 1 g/kg NaB led to a significant improvement in animal survival and a reduction in intestinal lesions compared to either compound alone in a chicken model of NE.Notably,the cecal microbiota of NE-infected chickens showed a marked decrease in SCFA-producing bacteria such as Bacteroides,Faecalibacterium,and Cuneatibacter,with lactobacilli becoming the most dominant species.However,supplementation with DCA and NaB largely restored the intestinal microbiota to healthy levels.Conclusions DCA synergizes with NaB to induce HDP and claudin-1 expression and enhance NE resistance,with potential for further development as cost-effective antibiotic alternatives.

Machine Learning Security Defense Algorithms Based on Metadata Correlation Features

With the popularization of the Internet and the development of technology,cyber threats are increasing day by day.Threats such as malware,hacking,and data breaches have had a serious impact on cybersecurity.The network security environment in the era of big data presents the characteristics of large amounts of data,high diversity,and high real-time requirements.Traditional security defense methods and tools have been unable to cope with the complex and changing network security threats.This paper proposes a machine-learning security defense algorithm based on metadata association features.Emphasize control over unauthorized users through privacy,integrity,and availability.The user model is established and the mapping between the user model and the metadata of the data source is generated.By analyzing the user model and its corresponding mapping relationship,the query of the user model can be decomposed into the query of various heterogeneous data sources,and the integration of heterogeneous data sources based on the metadata association characteristics can be realized.Define and classify customer information,automatically identify and perceive sensitive data,build a behavior audit and analysis platform,analyze user behavior trajectories,and complete the construction of a machine learning customer information security defense system.The experimental results show that when the data volume is 5×103 bit,the data storage integrity of the proposed method is 92%.The data accuracy is 98%,and the success rate of data intrusion is only 2.6%.It can be concluded that the data storage method in this paper is safe,the data accuracy is always at a high level,and the data disaster recovery performance is good.This method can effectively resist data intrusion and has high air traffic control security.It can not only detect all viruses in user data storage,but also realize integrated virus processing,and further optimize the security defense effect of user big data.

Adversarial attacks and defenses for digital communication signals identification

As modern communication technology advances apace,the digital communication signals identification plays an important role in cognitive radio networks,the communication monitoring and management systems.AI has become a promising solution to this problem due to its powerful modeling capability,which has become a consensus in academia and industry.However,because of the data-dependence and inexplicability of AI models and the openness of electromagnetic space,the physical layer digital communication signals identification model is threatened by adversarial attacks.Adversarial examples pose a common threat to AI models,where well-designed and slight perturbations added to input data can cause wrong results.Therefore,the security of AI models for the digital communication signals identification is the premise of its efficient and credible applications.In this paper,we first launch adversarial attacks on the end-to-end AI model for automatic modulation classifi-cation,and then we explain and present three defense mechanisms based on the adversarial principle.Next we present more detailed adversarial indicators to evaluate attack and defense behavior.Finally,a demonstration verification system is developed to show that the adversarial attack is a real threat to the digital communication signals identification model,which should be paid more attention in future research.

Multifunctional HDPE/Cu biocidal nanocomposites for MEX additive manufactured parts: Perspectives for the defense industry

In this study, we investigated the performance improvement caused by the addition of copper(Cu)nanoparticles to high-density polyethylene(HDPE) matrix material. Composite materials, with filler percentages of 0.0, 2.0, 4.0, 6.0, 8.0, and 10.0 wt% were synthesized through the material extrusion(MEX)3D printing technique. The synthesized nanocomposite filaments were utilized for the manufacturing of specimens suitable for the experimental procedure that followed. Hence, we were able to systematically investigate their tensile, flexural, impact, and microhardness properties through various mechanical tests that were conducted according to the corresponding standards. Broadband Dielectric Spectroscopy was used to investigate the electrical/dielectric properties of the composites. Moreover, by employing means of Raman spectroscopy and thermogravimetric analysis(TGA) we were also able to further investigate their vibrational, structural, and thermal properties. Concomitantly, means of scanning electron microscopy(SEM), as well as atomic force microscopy(AFM), were used for the examination of the morphological and structural characteristics of the synthesized specimens, while energy-dispersive Xray spectroscopy(EDS) was also performed in order to receive a more detailed picture on the structural characteristics of the various synthesized composites. The corresponding nanomaterials were also assessed for their antibacterial properties regarding Staphylococcus aureus(S. aureus) and Escherichia coli(E. coli) with the assistance of a method named screening agar well diffusion. The results showed that the mechanical properties of HDPE benefited from the utilization of Cu as a filler, as they showed a notable improvement. The specimen of HDPE/Cu 4.0 wt% was the one that presented the highest levels of reinforcement in four out of the seven tested mechanical properties(for example, it exhibited a 36.7%improvement in the flexural strength, compared to the pure matrix). At the same time, the nanocomposites were efficient against the S. aureus bacterium and less efficient against the E. coli bacterium.The use of such multi-functional, robust nanocomposites in MEX 3D printing is positively impacting applications in various fields, most notably in the defense and security sectors. The latter becomes increasingly important if one takes into account that most firearms encompass various polymeric parts that require robustness and improved mechanical properties, while at the same time keeping the risk of spreading various infectious microorganisms at a bare minimum.

Adaptive Network Sustainability and Defense Based on Artificial Bees Colony Optimization Algorithm for Nature Inspired Cyber Security

Cyber Defense is becoming a major issue for every organization to keep business continuity intact.The presented paper explores the effectiveness of a meta-heuristic optimization algorithm-Artificial Bees Colony Algorithm(ABC)as an Nature Inspired Cyber Security mechanism to achieve adaptive defense.It experiments on the Denial-Of-Service attack scenarios which involves limiting the traffic flow for each node.Businesses today have adapted their service distribution models to include the use of the Internet,allowing them to effectively manage and interact with their customer data.This shift has created an increased reliance on online services to store vast amounts of confidential customer data,meaning any disruption or outage of these services could be disastrous for the business,leaving them without the knowledge to serve their customers.Adversaries can exploit such an event to gain unauthorized access to the confidential data of the customers.The proposed algorithm utilizes an Adaptive Defense approach to continuously select nodes that could present characteristics of a probable malicious entity.For any changes in network parameters,the cluster of nodes is selected in the prepared solution set as a probable malicious node and the traffic rate with the ratio of packet delivery is managed with respect to the properties of normal nodes to deliver a disaster recovery plan for potential businesses.

Effective Utilization of Government-Provided CTI by Small Businesses within the Defense Industrial Base

There are two broad objectives of the research reported in this paper. First, we assess whether government-provided cyber threat intelligence (CTI) is helpful in preventing, or responding to, cyber-attacks among small businesses within the U.S. Defense Industrial Base (DIB). Second, we identify ways of improving the effectiveness of government-provided CTI to small businesses within the DIB. Based on a questionnaire-based survey, our findings suggest that government-provided CTI helps businesses within the DIB in preventing, or responding to, cyber-attacks providing a firm is familiar with the CTI. Unfortunately, a large percentage of small firms are not familiar with the government-provided CTI feeds and consequently are not utilizing the CTI. This latter situation is largely due to financial constraints confronting small businesses that prevent firms from having the wherewithal necessary to effectively utilize the government-provided CTI. However, we found a significant positive association between a firm’s familiarity with the government-provided CTI and whether a firm is being periodically reviewed by the Defense Counterintelligence and Security Agency (DCSA) or is compliant with the Cybersecurity Maturity Model Certification (CMMC) program. The findings from our study also show that the participating firms believe that external cyber threats are more likely to be the cause of a future cybersecurity breach than internal cybersecurity threats. Finally, our study found that the portion of the IT budget that small businesses within the DIB spend on cybersecurity-related activities is dependent on the perception that a firm would be the target of an external cyber-attack.

Beyond Defense: Proactive Approaches to Disaster Recovery and Threat Intelligence in Modern Enterprises

As cyber threats keep changing and business environments adapt, a comprehensive approach to disaster recovery involves more than just defensive measures. This research delves deep into the strategies required to respond to threats and anticipate and mitigate them proactively. Beginning with understanding the critical need for a layered defense and the intricacies of the attacker’s journey, the research offers insights into specialized defense techniques, emphasizing the importance of timely and strategic responses during incidents. Risk management is brought to the forefront, underscoring businesses’ need to adopt mature risk assessment practices and understand the potential risk impact areas. Additionally, the value of threat intelligence is explored, shedding light on the importance of active engagement within sharing communities and the vigilant observation of adversary motivations. “Beyond Defense: Proactive Approaches to Disaster Recovery and Threat Intelligence in Modern Enterprises” is a comprehensive guide for organizations aiming to fortify their cybersecurity posture, marrying best practices in proactive and reactive measures in the ever-challenging digital realm.

GUARDIAN: A Multi-Tiered Defense Architecture for Thwarting Prompt Injection Attacks on LLMs

This paper introduces a novel multi-tiered defense architecture to protect language models from adversarial prompt attacks. We construct adversarial prompts using strategies like role emulation and manipulative assistance to simulate real threats. We introduce a comprehensive, multi-tiered defense framework named GUARDIAN (Guardrails for Upholding Ethics in Language Models) comprising a system prompt filter, pre-processing filter leveraging a toxic classifier and ethical prompt generator, and pre-display filter using the model itself for output screening. Extensive testing on Meta’s Llama-2 model demonstrates the capability to block 100% of attack prompts. The approach also auto-suggests safer prompt alternatives, thereby bolstering language model security. Quantitatively evaluated defense layers and an ethical substitution mechanism represent key innovations to counter sophisticated attacks. The integrated methodology not only fortifies smaller LLMs against emerging cyber threats but also guides the broader application of LLMs in a secure and ethical manner.

Attacks Against Cross-Chain Systems and Defense Approaches:A Contemporary Survey

The blockchain cross-chain is a significant technology for inter-chain interconnection and value transfer among different blockchain networks.Cross-chain overcomes the“information island”problem of the closed blockchain network and is increasingly applied to multiple critical areas such as finance and the internet of things(IoT).Blockchain can be divided into three main categories of blockchain networks:public blockchains,private blockchains,and consortium blockchains.However,there are differences in block structures,consensus mechanisms,and complex working mechanisms among heterogeneous blockchains.The fragility of the cross-chain system itself makes the cross-chain system face some potential security and privacy threats.This paper discusses security defects on the cross-chain implementation mechanism,and discusses the impact of the structural features of blockchain networks on cross-chain security.In terms of cross-chain intercommunication,a cross-chain attack can be divided into a multi-chain combination attack,native chain attack,and inter-chain attack diffusion.Then various security threats and attack paths faced by the cross-chain system are analyzed.At last,the corresponding security defense methods of cross-chain security threats and future research directions for cross-chain applications are put forward.

Chained Dual-Generative Adversarial Network:A Generalized Defense Against Adversarial Attacks

Neural networks play a significant role in the field of image classification.When an input image is modified by adversarial attacks,the changes are imperceptible to the human eye,but it still leads to misclassification of the images.Researchers have demonstrated these attacks to make production self-driving cars misclassify StopRoad signs as 45 Miles Per Hour(MPH)road signs and a turtle being misclassified as AK47.Three primary types of defense approaches exist which can safeguard against such attacks i.e.,Gradient Masking,Robust Optimization,and Adversarial Example Detection.Very few approaches use Generative Adversarial Networks(GAN)for Defense against Adversarial Attacks.In this paper,we create a new approach to defend against adversarial attacks,dubbed Chained Dual-Generative Adversarial Network(CD-GAN)that tackles the defense against adversarial attacks by minimizing the perturbations of the adversarial image using iterative oversampling and undersampling using GANs.CD-GAN is created using two GANs,i.e.,CDGAN’s Sub-ResolutionGANandCDGAN’s Super-ResolutionGAN.The first is CDGAN’s Sub-Resolution GAN which takes the original resolution input image and oversamples it to generate a lower resolution neutralized image.The second is CDGAN’s Super-Resolution GAN which takes the output of the CDGAN’s Sub-Resolution and undersamples,it to generate the higher resolution image which removes any remaining perturbations.Chained Dual GAN is formed by chaining these two GANs together.Both of these GANs are trained independently.CDGAN’s Sub-Resolution GAN is trained using higher resolution adversarial images as inputs and lower resolution neutralized images as output image examples.Hence,this GAN downscales the image while removing adversarial attack noise.CDGAN’s Super-Resolution GAN is trained using lower resolution adversarial images as inputs and higher resolution neutralized images as output images.Because of this,it acts as an Upscaling GAN while removing the adversarial attak noise.Furthermore,CD-GAN has a modular design such that it can be prefixed to any existing classifier without any retraining or extra effort,and 2542 CMC,2023,vol.74,no.2 can defend any classifier model against adversarial attack.In this way,it is a Generalized Defense against adversarial attacks,capable of defending any classifier model against any attacks.This enables the user to directly integrate CD-GANwith an existing production deployed classifier smoothly.CD-GAN iteratively removes the adversarial noise using a multi-step approach in a modular approach.It performs comparably to the state of the arts with mean accuracy of 33.67 while using minimal compute resources in training.

Novel 18β-glycyrrhetinic acid amide derivatives show dual-acting capabilities for controlling plant bacterial diseases through ROS-mediated antibacterial efficiency and activating plant defense responses

Natural products have long been a crucial source of,or provided inspiration for new agrochemical discovery.Naturally occurring 18β-glycyrrhetinic acid shows broad-spectrum bioactivities and is a potential skeleton for novel drug discovery.To extend the utility of 18β-glycyrrhetinic acid for agricultural uses,a series of novel 18β-glycyrrhetinic acid amide derivatives were prepared and evaluated for their antibacterial potency.Notably,compound 5k showed good antibacterial activity in vitro against Xanthomonas oryzae pv.oryzae(Xoo,EC50=3.64 mg L–1),and excellent protective activity(54.68%)against Xoo in vivo.Compound 5k induced excessive production and accumulation of reactive oxygen species in the tested pathogens,resulting in damaging the bacterial cell envelope.More interestingly,compound 5k could increase the activities of plant defense enzymes including catalase,superoxide dismutase,peroxidase,and phenylalanine ammonia lyase.Taken together,these enjoyable results suggested that designed compounds derived from 18β-glycyrrhetinic acid showed potential for controlling intractable plant bacterial diseases by disturbing the balance of the phytopathogen’s redox system and activating the plant defense system.

An Air Defense Weapon Target Assignment Method Based on Multi-Objective Artificial Bee Colony Algorithm

With the advancement of combat equipment technology and combat concepts,new requirements have been put forward for air defense operations during a group target attack.To achieve high-efficiency and lowloss defensive operations,a reasonable air defense weapon assignment strategy is a key step.In this paper,a multi-objective and multi-constraints weapon target assignment(WTA)model is established that aims to minimize the defensive resource loss,minimize total weapon consumption,and minimize the target residual effectiveness.An optimization framework of air defense weapon mission scheduling based on the multiobjective artificial bee colony(MOABC)algorithm is proposed.The solution for point-to-point saturated attack targets at different operational scales is achieved by encoding the nectar with real numbers.Simulations are performed for an imagined air defense scenario,where air defense weapons are saturated.The non-dominated solution sets are obtained by the MOABC algorithm to meet the operational demand.In the case where there are more weapons than targets,more diverse assignment schemes can be selected.According to the inverse generation distance(IGD)index,the convergence and diversity for the solutions of the non-dominated sorting genetic algorithm III(NSGA-III)algorithm and the MOABC algorithm are compared and analyzed.The results prove that the MOABC algorithm has better convergence and the solutions are more evenly distributed among the solution space.