The rapid growth of Internet of things devices and the emergence of rapidly evolving network threats have made traditional security assessment methods inadequate.Federated learning offers a promising solution to exped...The rapid growth of Internet of things devices and the emergence of rapidly evolving network threats have made traditional security assessment methods inadequate.Federated learning offers a promising solution to expedite the training of security assessment models.However,ensuring the trustworthiness and robustness of federated learning under multi-party collaboration scenarios remains a challenge.To address these issues,this study proposes a shard aggregation network structure and a malicious node detection mechanism,along with improvements to the federated learning training process.First,we extract the data features of the participants by using spectral clustering methods combined with a Gaussian kernel function.Then,we introduce a multi-objective decision-making approach that combines data distribution consistency,consensus communication overhead,and consensus result reliability in order to determine the final network sharing scheme.Finally,by integrating the federated learning aggregation process with the malicious node detection mechanism,we improve the traditional decentralized learning process.Our proposed ShardFed algorithm outperforms conventional classification algorithms and state-of-the-art machine learning methods like FedProx and FedCurv in convergence speed,robustness against data interference,and adaptability across multiple scenarios.Experimental results demonstrate that the proposed approach improves model accuracy by up to 2.33%under non-independent and identically distributed data conditions,maintains higher performance with malicious nodes containing poisoned data ratios of 20%–50%,and significantly enhances model resistance to low-quality data.展开更多
The key parameters that characterize the morphological quality of multi-layer and multi-pass metal laser deposited parts are the surface roughness and the error between the actual printing height and the theoretical m...The key parameters that characterize the morphological quality of multi-layer and multi-pass metal laser deposited parts are the surface roughness and the error between the actual printing height and the theoretical model height.The Taguchi method was employed to establish the correlations between process parameter combinations and multi-objective characterization of metal deposition morphology(height error and roughness).Results show that using the signal-to-noise ratio and grey relational analysis,the optimal parameter combination for multi-layer and multi-pass deposition is determined as follows:laser power of 800 W,powder feeding rate of 0.3 r/min,step distance of 1.6 mm,and scanning speed of 20 mm/s.Subsequently,a Genetic Bayesian-back propagation(GB-BP)network is constructed to predict multi-objective responses.Compared with the traditional back propagation network,the GB-back propagation network improves the prediction accuracy of height error and surface roughness by 43.14%and 71.43%,respectively.This network can accurately predict the multi-objective characterization of morphological quality of multi-layer and multi-pass metal deposited parts.展开更多
Large language models(LLMs)have revolutionized AI applications across diverse domains.However,their widespread deployment has introduced critical security vulnerabilities,particularly prompt injection attacks that man...Large language models(LLMs)have revolutionized AI applications across diverse domains.However,their widespread deployment has introduced critical security vulnerabilities,particularly prompt injection attacks that manipulate model behavior through malicious instructions.Following Kitchenham’s guidelines,this systematic review synthesizes 128 peer-reviewed studies from 2022 to 2025 to provide a unified understanding of this rapidly evolving threat landscape.Our findings reveal a swift progression from simple direct injections to sophisticated multimodal attacks,achieving over 90%success rates against unprotected systems.In response,defense mechanisms show varying effectiveness:input preprocessing achieves 60%–80%detection rates and advanced architectural defenses demonstrate up to 95%protection against known patterns,though significant gaps persist against novel attack vectors.We identified 37 distinct defense approaches across three categories,but standardized evaluation frameworks remain limited.Our analysis attributes these vulnerabilities to fundamental LLM architectural limitations,such as the inability to distinguish instructions from data and attention mechanism vulnerabilities.This highlights critical research directions such as formal verification methods,standardized evaluation protocols,and architectural innovations for inherently secure LLM designs.展开更多
Federated Learning(FL)enables joint training over distributed devices without data exchange but is highly vulnerable to attacks by adversaries in the form of model poisoning and malicious update injection.This work pr...Federated Learning(FL)enables joint training over distributed devices without data exchange but is highly vulnerable to attacks by adversaries in the form of model poisoning and malicious update injection.This work proposes Secured-FL,a blockchain-based defensive framework that combines smart contract-based authentication,clustering-driven outlier elimination,and dynamic threshold adjustment to defend against adversarial attacks.The framework was implemented on a private Ethereum network with a Proof-of-Authority consensus algorithm to ensure tamper-resistant and auditable model updates.Large-scale simulation on the Cyber Data dataset,under up to 50%malicious client settings,demonstrates Secured-FL achieves 6%-12%higher accuracy,9%-15%lower latency,and approximately 14%less computational expense compared to the PPSS benchmark framework.Additional tests,including confusion matrices,ROC and Precision-Recall curves,and ablation tests,confirm the interpretability and robustness of the defense.Tests for scalability also show consistent performance up to 500 clients,affirming appropriateness to reasonably large deployments.These results make Secured-FL a feasible,adversarially resilient FL paradigm with promising potential for application in smart cities,medicine,and other mission-critical IoT deployments.展开更多
Brown spot(BS)of rice,caused by Bipolaris oryzae,is a serious concern that not only causes quantitative losses but also affects grain quality.To manage this disease,the use of resistant genetic sources and QTLs is an ...Brown spot(BS)of rice,caused by Bipolaris oryzae,is a serious concern that not only causes quantitative losses but also affects grain quality.To manage this disease,the use of resistant genetic sources and QTLs is an eco-friendly and economical option.In the current study,F_(3) progenies derived from a cross of susceptible parent PMS-18-B(PAU 10845-1-1-1-1)×resistant parent RP Path 77(RP patho-17)were used to identify potential QTLs linked to BS resistance and to associate this resistance with a temporal spike in defense-related enzymes.展开更多
At present,the emerging solid-phase friction-based additive manufacturing technology,including friction rolling additive man-ufacturing(FRAM),can only manufacture simple single-pass components.In this study,multi-laye...At present,the emerging solid-phase friction-based additive manufacturing technology,including friction rolling additive man-ufacturing(FRAM),can only manufacture simple single-pass components.In this study,multi-layer multi-pass FRAM-deposited alumin-um alloy samples were successfully prepared using a non-shoulder tool head.The material flow behavior and microstructure of the over-lapped zone between adjacent layers and passes during multi-layer multi-pass FRAM deposition were studied using the hybrid 6061 and 5052 aluminum alloys.The results showed that a mechanical interlocking structure was formed between the adjacent layers and the adja-cent passes in the overlapped center area.Repeated friction and rolling of the tool head led to different degrees of lateral flow and plastic deformation of the materials in the overlapped zone,which made the recrystallization degree in the left and right edge zones of the over-lapped zone the highest,followed by the overlapped center zone and the non-overlapped zone.The tensile strength of the overlapped zone exceeded 90%of that of the single-pass deposition sample.It is proved that although there are uneven grooves on the surface of the over-lapping area during multi-layer and multi-pass deposition,they can be filled by the flow of materials during the deposition of the next lay-er,thus ensuring the dense microstructure and excellent mechanical properties of the overlapping area.The multi-layer multi-pass FRAM deposition overcomes the limitation of deposition width and lays the foundation for the future deposition of large-scale high-performance components.展开更多
Heat stress hinders the growth and productivity of sweetpotato plants,predominantly through oxidative damage to cellular membranes.Therefore,the development of efficient approaches for mitigating heat-related impairme...Heat stress hinders the growth and productivity of sweetpotato plants,predominantly through oxidative damage to cellular membranes.Therefore,the development of efficient approaches for mitigating heat-related impairments is essential for the long-term production of sweetpotatoes.Melatonin has been recognised for its capacity to assist plants in dealing with abiotic stress conditions.This research aimed to investigate how different doses of exogenous melatonin influence heat damage in sweetpotato plants.Heat stress drastically affected shoot and root fresh weight by 31.8 and 44.5%,respectively.This reduction resulted in oxidative stress characterised by increased formation of hydrogen peroxide(H_(2)O_(2))by 804.4%,superoxide ion(O_(2)^(·-))by 211.5%and malondialdehyde(MDA)by 234.2%.Heat stress also reduced chlorophyll concentration,photosystemⅡefficiency(F_v/F_m)by 15.3%and gaseous exchange.However,pre-treatment with 100μmol L^(-1)melatonin increased growth and reduced oxidative damage to sweetpotato plants under heat stress.In particular,melatonin decreased H_(2)O_(2),O_(2)^(·-)and MDA by 64.8%,42.7%and 38.2%,respectively.Melatonin also mitigated the decline in chlorophyll levels and improved stomatal traits,gaseous exchange and F_(v)/F_(m)(13%).Results suggested that the favorable outcomes of melatonin treatment can be associated with elevated antioxidant enzyme activity and an increase in non-enzymatic antioxidants and osmo-protectants.Overall,these findings indicate that exogenous melatonin can improve heat stress tolerance in sweetpotatoes.This stu dy will assist re searchers in further investigating how melatonin makes sweetpotatoes more resistant to heat stress.展开更多
The Industrial Internet of Things(IIoT)is increasingly vulnerable to sophisticated cyber threats,particularly zero-day attacks that exploit unknown vulnerabilities and evade traditional security measures.To address th...The Industrial Internet of Things(IIoT)is increasingly vulnerable to sophisticated cyber threats,particularly zero-day attacks that exploit unknown vulnerabilities and evade traditional security measures.To address this critical challenge,this paper proposes a dynamic defense framework named Zero-day-aware Stackelberg Game-based Multi-Agent Distributed Deep Deterministic Policy Gradient(ZSG-MAD3PG).The framework integrates Stackelberg game modeling with the Multi-Agent Distributed Deep Deterministic Policy Gradient(MAD3PG)algorithm and incorporates defensive deception(DD)strategies to achieve adaptive and efficient protection.While conventional methods typically incur considerable resource overhead and exhibit higher latency due to static or rigid defensive mechanisms,the proposed ZSG-MAD3PG framework mitigates these limitations through multi-stage game modeling and adaptive learning,enabling more efficient resource utilization and faster response times.The Stackelberg-based architecture allows defenders to dynamically optimize packet sampling strategies,while attackers adjust their tactics to reach rapid equilibrium.Furthermore,dynamic deception techniques reduce the time required for the concealment of attacks and the overall system burden.A lightweight behavioral fingerprinting detection mechanism further enhances real-time zero-day attack identification within industrial device clusters.ZSG-MAD3PG demonstrates higher true positive rates(TPR)and lower false alarm rates(FAR)compared to existing methods,while also achieving improved latency,resource efficiency,and stealth adaptability in IIoT zero-day defense scenarios.展开更多
The defense mechanisms induced in wild Chinese pine(Pinus tabuliformis)in response to herbivores are not well characterized,especially in the field.To address this knowledge gap,we established a biological model syste...The defense mechanisms induced in wild Chinese pine(Pinus tabuliformis)in response to herbivores are not well characterized,especially in the field.To address this knowledge gap,we established a biological model system to evaluate proteome variations in pine needles after feeding by the Chinese pine caterpillar(Dendrolimus tabulaeformis),a major natural enemy and dominant herbivore.Quantitative tandem mass tag(TMT)proteomics and bioinformatics were utilized to systematically identify differentially abundant proteins implicated in the induced defense response of Chinese pine.We validated key protein changes using parallel reaction monitoring(PRM)technology.Pathway analysis revealed that the induced defenses involved phenylpropanoid,coumarin,and flavonoid biosynthesis,among other processes.To elucidate the regulatory patterns underlying pine resistance,we determined the activities of defense enzymes and levels of physiological and biochemical compounds.In addition,the expression of upstream genes for key proteins was validated by qRT-PCR.Our results provide new molecular insights into the induced defense mechanisms in Chinese pine against this caterpillar in the field.A better understanding of these defense strategies will inform efforts to breed more-resistant pine varieties.展开更多
Due to the discharge of industrialwastewater,urban domestic sewage,and intensive marine aquaculture tailwater,nitrate(NO_(3)^(−))pollution has emerged as a significant issue in offshore waters.Nitrate pollution affect...Due to the discharge of industrialwastewater,urban domestic sewage,and intensive marine aquaculture tailwater,nitrate(NO_(3)^(−))pollution has emerged as a significant issue in offshore waters.Nitrate pollution affects aquatic life and may interact with other pollutants,leading to comprehensive toxicity.Cadmium(Cd^(2+))is the most widespread metal contaminant,adversely affecting aquatic life in the coastal waters of China.Despite this,few studies have focused on the synergistic toxicity of NO_(3)^(−)and Cd^(2+)in marine organisms.This study conducted a 30-day exposure experiment on marine Japanese flounder(Paralichthys olivaceus)to explore the synergistic toxicity of NO_(3)^(−)and Cd^(2+).Our results demonstrated that the exposure to Cd^(2+)alone induced slight histopathological changes in the liver.However,malformations such as hepatic vacuolar degeneration and sinusoid dilatationwere exacerbated under co-exposure.Moreover,co-exposure induced the downregulation of antioxidants and the upregulation of the product malonaldehyde(MDA)from lipid peroxidation,indicating potent oxidative stress in the liver.The increased mRNA expression of IL-8,TNF-α,and IL-1β,along with the decreased expression level of TGF-β,indicated a synergistic inflammatory response in the organisms.Furthermore,the co-exposure led to an abnormal expression of P53,caspase-3,caspase-9,Bcl-2,and Bax,and disturbed the apoptosis in the liver through TUNEL staining analysis.Overall,our results imply that co-exposure synergistically affects inflammation,redox status,and apoptosis in flounders.Therefore,the findings from this study provide valuable perspectives on the ecological risk assessment of marine teleosts co-exposure to NO_(3)^(−)and Cd^(2+).展开更多
Benzoxazinoids(BXDs)are a class of plant secondary metabolites that play pivotal roles in plant defense against pathogens and pests,as well as in allelopathy.This review synthesizes recent advances in our understandin...Benzoxazinoids(BXDs)are a class of plant secondary metabolites that play pivotal roles in plant defense against pathogens and pests,as well as in allelopathy.This review synthesizes recent advances in our understanding of the structural and functional diversity of BXDs,the independent evolutionary trajectories of their biosynthetic pathways across different plant species,their metabolic transformations in target organisms,and the opportunities and challenges of optimizing BXD biosynthesis in crops through metabolic engineering.Compared with monocotyledons,dicotyledons employ a more diverse set of enzymes to catalyze the core reactions of BXD biosynthesis.This functional divergence—yet biochemical convergence—between monocotyledons and dicotyledons exemplifies the convergent evolution of BXD biosynthetic pathways in plants.BXDs act not only as potent antifeedants,insecticides,and antimicrobials but also function as signaling molecules that induce callose deposition and activate systemic immunity,thereby enhancing plant resistance to biotic stress.Furthermore,BXDs shape the rhizosphere by modulating microbial communities through species-specific antimicrobial activities and microbial detoxification mechanisms,ultimately exerting allelopathic effects that alter soil chemistry and nutrient dynamics.The translational potential of BXDs is increasingly recognized by synthetic biology approaches,including artificial intelligence-driven enzyme optimization,heterologous pathway engineering,and gene-editing to enhance crop resistance.Despite these promising prospects,challenges remain in balancing metabolic trade-offs and mitigating ecological risks associated with persistent accumulation of BXDs.Future research integrating multi-omics,evolutionary genomics,and microbiome studies will be essential to fully harness BXDs for sustainable crop improvement and reduced reliance on synthetic agrochemicals.展开更多
This paper investigates the number of limit cycles in a predator-prey system with group defense,intially introduced by Wolkowicz and later examined by Rothe and Shafer in the 1980’s.Under the assumption of large prey...This paper investigates the number of limit cycles in a predator-prey system with group defense,intially introduced by Wolkowicz and later examined by Rothe and Shafer in the 1980’s.Under the assumption of large prey growth,the system reduces to a perturbed singular system,whose limit cycles can be analyzed using geometric singular perturbation methods-primarily through the study of a slow-divergence integral.Our work completes partially the results previously obtained by Li and Zhu and by Hsu.We provide a comprehensive classification of all possible singular cycles capable of generating limit cycles and analyze the slow-divergence integral for the nine distinct types of cycle families that arise in a canard explosion.Based on these findings,we demonstrate that the maximum number of limit cycles emerging from the singular cycles is two in all cases,thereby confirming conjectures posed by Rothe-Shafer and Xiao-Ruan.展开更多
Nanoplastics(less than 1µm in size,NPs)have emerged as a significant pollutant in aquatic environment,posing considerable threats to freshwater biota.However,the mechanisms through which NPs modulate the predatio...Nanoplastics(less than 1µm in size,NPs)have emerged as a significant pollutant in aquatic environment,posing considerable threats to freshwater biota.However,the mechanisms through which NPs modulate the predation responses of these organisms remain poorly elucidated.We investigated the impacts of polystyrene NPs,characterized by a representative particle size(diameter:50 nm;concentration:0–8μg/L),on the anti-predation defense mechanisms of mature rotifer Brachionus calyciflorus against predator of rotifer Asplanchna brightwellii,utilizing transcriptomics to unravel the underlying molecular pathways.Results reveal that the posterolateral spine length and type of B.calyciflorus serve as robust indicators of defensive morphology,even in the presence of NPs exposure.Specifically,increasing concentrations of NPs and predator cues suppressed the defensive responses,which was associated with morphological transformations.This suppression was associated with the down-regulation of the HIF-1αsignaling pathway,implicating potentially its role in modulating fight-or-flight responses.Furthermore,we identified functional crosstalk among multiple signaling pathways,including HIF-1α,PI3K-Akt,FoxO,and mTOR,in B.calyciflorus,which may underpin the organism's responses to polystyrene NP exposure.These findings contribute to the advancement of predictive models to assess the ecological risks posed by polystyrene NPs contamination in aquatic ecosystems.展开更多
Recent years have witnessed the ever-increasing performance of Deep Neural Networks(DNNs)in computer vision tasks.However,researchers have identified a potential vulnerability:carefully crafted adversarial examples ca...Recent years have witnessed the ever-increasing performance of Deep Neural Networks(DNNs)in computer vision tasks.However,researchers have identified a potential vulnerability:carefully crafted adversarial examples can easily mislead DNNs into incorrect behavior via the injection of imperceptible modification to the input data.In this survey,we focus on(1)adversarial attack algorithms to generate adversarial examples,(2)adversarial defense techniques to secure DNNs against adversarial examples,and(3)important problems in the realm of adversarial examples beyond attack and defense,including the theoretical explanations,trade-off issues and benign attacks in adversarial examples.Additionally,we draw a brief comparison between recently published surveys on adversarial examples,and identify the future directions for the research of adversarial examples,such as the generalization of methods and the understanding of transferability,that might be solutions to the open problems in this field.展开更多
Deep neural networks are known to be vulnerable to adversarial attacks.Unfortunately,the underlying mechanisms remain insufficiently understood,leading to empirical defenses that often fail against new attacks.In this...Deep neural networks are known to be vulnerable to adversarial attacks.Unfortunately,the underlying mechanisms remain insufficiently understood,leading to empirical defenses that often fail against new attacks.In this paper,we explain adversarial attacks from the perspective of robust features,and propose a novel Generative Adversarial Network(GAN)-based Robust Feature Disentanglement framework(GRFD)for adversarial defense.The core of GRFD is an adversarial disentanglement structure comprising a generator and a discriminator.For the generator,we introduce a novel Latent Variable Constrained Variational Auto-Encoder(LVCVAE),which enhances the typical beta-VAE with a constrained rectification module to enforce explicit clustering of latent variables.To supervise the disentanglement of robust features,we design a Robust Supervisory Model(RSM)as the discriminator,sharing architectural alignment with the target model.The key innovation of RSM is our proposed Feature Robustness Metric(FRM),which serves as part of the training loss and synthesizes the classification ability of features as well as their resistance to perturbations.Extensive experiments on three benchmark datasets demonstrate the superiority of GRFD:it achieves 93.69%adversarial accuracy on MNIST,77.21%on CIFAR10,and 58.91%on CIFAR100 with minimal degradation in clean accuracy.Codes are available at:(accessed on 23 July 2025).展开更多
The huge impact kinetic energy cannot be quickly dissipated by the energy-absorbing structure and transferred to the other vehicle through the car body structure,which will cause structural damage and threaten the liv...The huge impact kinetic energy cannot be quickly dissipated by the energy-absorbing structure and transferred to the other vehicle through the car body structure,which will cause structural damage and threaten the lives of the occupants.Therefore,it is necessary to understand the laws of energy conversion,dissipation and transfer during train collisions.This study proposes a multi-layer progressive analysis method of energy flow during train collisions,considering the characteristics of the train.In this method,the train collision system is divided into conversion,dissipation,and transfer layers from the perspective of the train,collision interface,and car body structure to analyze the energy conversion,dissipation and transfer characteristics.Taking the collision process of a rail train as an example,a train collision energy transfer path analysis model was established based on power flow theory.The results show that when the maximum mean acceleration of the vehicle meets the standard requirements,the jerk may exceed the allowable limit of the human body,and there is a risk of injury to the occupants of a secondary collision.The decay rate of the collision energy along the direction of train operation reaches 79%.As the collision progresses,the collision energy gradually converges in the structure with holes,and the structure deforms when the gathered energy is greater than the maximum energy the structure can withstand.The proposed method helps to understand the train collision energy flow law and provides theoretical support for the train crashworthiness design in the future.展开更多
Aiming at the terminal defense problem of aircraft,this paper proposes a method to simultaneously achieve terminal defense and seize the dominant position.The method employs aλ-return based reinforcement learning alg...Aiming at the terminal defense problem of aircraft,this paper proposes a method to simultaneously achieve terminal defense and seize the dominant position.The method employs aλ-return based reinforcement learning algorithm,which can be applied to the flight assistance decision-making system to improve the pilot’s survivability.First,we model the environment to simulate the interaction between air-to-air missiles and aircraft.Subsequently,we propose aλ-return based approach to improve the deep Q learning network(DQN),deep advantageous actor criticism(A2C),and proximity policy optimization(PPO)algorithms used to train manoeuvre strategies.The method employs an action space containing nine manoeuvres and defines the off-target distance at the end of the scene as a sparse reward for algorithm training.Simulation results show that the convergence speed of the three improved algorithms is significantly improved when using theλ-return method.Moreover,the effect of the fetch value on the convergence speed is verified by ablation experiments.In order to solve the illegal behavior problem in the training process,we also design a backtracking-based illegal behavior masking mechanism,which improves the data generation efficiency of the environment model and promotes effective algorithm training.展开更多
The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems(IDS)for enhanced network security.While conventional IDSs can be unsuitable for detecting different and emerging at...The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems(IDS)for enhanced network security.While conventional IDSs can be unsuitable for detecting different and emerging attacks,there is a demand for better techniques to improve detection reliability.This study introduces a new method,the Deep Adaptive Multi-Layer Attention Network(DAMLAN),to boost the result of intrusion detection on network data.Due to its multi-scale attention mechanisms and graph features,DAMLAN aims to address both known and unknown intrusions.The real-world NSL-KDD dataset,a popular choice among IDS researchers,is used to assess the proposed model.There are 67,343 normal samples and 58,630 intrusion attacks in the training set,12,833 normal samples,and 9711 intrusion attacks in the test set.Thus,the proposed DAMLAN method is more effective than the standard models due to the consideration of patterns by the attention layers.The experimental performance of the proposed model demonstrates that it achieves 99.26%training accuracy and 90.68%testing accuracy,with precision reaching 98.54%on the training set and 96.64%on the testing set.The recall and F1 scores again support the model with training set values of 99.90%and 99.21%and testing set values of 86.65%and 91.37%.These results provide a strong basis for the claims made regarding the model’s potential to identify intrusion attacks and affirm its relatively strong overall performance,irrespective of type.Future work would employ more attempts to extend the scalability and applicability of DAMLAN for real-time use in intrusion detection systems.展开更多
Unlike most plants, members of the genus Solanum produce cholesterol and use this as a precursor for steroidal glycoalkaloids. The production of the compounds begins as a branch from brassinosteroid biosynthesis, whic...Unlike most plants, members of the genus Solanum produce cholesterol and use this as a precursor for steroidal glycoalkaloids. The production of the compounds begins as a branch from brassinosteroid biosynthesis, which produces cholesterol that is further modified to produce steroidal glycoalkaloids. During the cholesterol biosynthesis pathway, genetic engineering could alter the formation of cholesterol from provitamin D3(7-dehydrocholesterol) and produce vitamin D3. Cholesterol is a precursor for many steroidal glycoalkaloids, including a-tomatine and esculeoside A. Alpha-tomatine is consumed by mammals and it can reduce cholesterol content and improve LDL:HDL ratio. When there is a high a-tomatine content, the fruit will have a bitter flavor, which together with other steroidal glycoalkaloids serving as protective and defensive compounds for tomato against insect, fungal, and bacterial pests. These compounds also affect the rhizosphere bacteria by recruiting beneficial bacteria. One of the steroidal glycoalkaloids, esculeoside A increases while fruit ripening. This review focuses on recent studies that uncovered key reactions of the production of cholesterol and steroidal glycoalkaloids in tomato connecting to human health, fruit flavor, and plant defense and the potential application for tomato crop improvement.展开更多
In practical engineering construction,multi-layered barriers containing geomembranes are extensively applied to retard the migration of pollutants.However,the associated analytical theory on pollutants diffusion still...In practical engineering construction,multi-layered barriers containing geomembranes are extensively applied to retard the migration of pollutants.However,the associated analytical theory on pollutants diffusion still needs to be further improved.In this work,general analytical solutions are derived for one-dimensional diffusion of degradable organic contaminant(DOC)in the multi-layered media containing geomembranes under a time-varying concentration boundary condition,where the variable substitution and separated variable approaches are employed.These analytical solutions with clear expressions can be used not only to study the diffusion behaviors of DOC in bottom and vertical composite barrier systems,but also to verify other complex numerical models.The proposed general analytical solutions are then fully validated via three comparative analyses,including comparisons with the experimental measurements,an existing analytical solution,and a finite-difference solution.Ultimately,the influences of different factors on the composite cutoff wall’s(CCW,which consists of two soil-bentonite layers and a geomembrane)service performance are investigated through a composite vertical barrier system as the application example.The findings obtained from this investigation can provide scientific guidance for the barrier performance evaluation and the engineering design of CCWs.This application example also exhibits the necessity and effectiveness of the developed analytical solutions.展开更多
基金supported by State Grid Hebei Electric Power Co.,Ltd.Science and Technology Project,Research on Security Protection of Power Services Carried by 4G/5G Networks(Grant No.KJ2024-127).
文摘The rapid growth of Internet of things devices and the emergence of rapidly evolving network threats have made traditional security assessment methods inadequate.Federated learning offers a promising solution to expedite the training of security assessment models.However,ensuring the trustworthiness and robustness of federated learning under multi-party collaboration scenarios remains a challenge.To address these issues,this study proposes a shard aggregation network structure and a malicious node detection mechanism,along with improvements to the federated learning training process.First,we extract the data features of the participants by using spectral clustering methods combined with a Gaussian kernel function.Then,we introduce a multi-objective decision-making approach that combines data distribution consistency,consensus communication overhead,and consensus result reliability in order to determine the final network sharing scheme.Finally,by integrating the federated learning aggregation process with the malicious node detection mechanism,we improve the traditional decentralized learning process.Our proposed ShardFed algorithm outperforms conventional classification algorithms and state-of-the-art machine learning methods like FedProx and FedCurv in convergence speed,robustness against data interference,and adaptability across multiple scenarios.Experimental results demonstrate that the proposed approach improves model accuracy by up to 2.33%under non-independent and identically distributed data conditions,maintains higher performance with malicious nodes containing poisoned data ratios of 20%–50%,and significantly enhances model resistance to low-quality data.
基金National Natural Science Foundation of China(52175237)。
文摘The key parameters that characterize the morphological quality of multi-layer and multi-pass metal laser deposited parts are the surface roughness and the error between the actual printing height and the theoretical model height.The Taguchi method was employed to establish the correlations between process parameter combinations and multi-objective characterization of metal deposition morphology(height error and roughness).Results show that using the signal-to-noise ratio and grey relational analysis,the optimal parameter combination for multi-layer and multi-pass deposition is determined as follows:laser power of 800 W,powder feeding rate of 0.3 r/min,step distance of 1.6 mm,and scanning speed of 20 mm/s.Subsequently,a Genetic Bayesian-back propagation(GB-BP)network is constructed to predict multi-objective responses.Compared with the traditional back propagation network,the GB-back propagation network improves the prediction accuracy of height error and surface roughness by 43.14%and 71.43%,respectively.This network can accurately predict the multi-objective characterization of morphological quality of multi-layer and multi-pass metal deposited parts.
基金supported by 2023 Higher Education Scientific Research Planning Project of China Society of Higher Education(No.23PG0408)2023 Philosophy and Social Science Research Programs in Jiangsu Province(No.2023SJSZ0993)+2 种基金Nantong Science and Technology Project(No.JC2023070)Key Project of Jiangsu Province Education Science 14th Five-Year Plan(Grant No.B-b/2024/02/41)the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province(Grant No.SKLACSS-202407).
文摘Large language models(LLMs)have revolutionized AI applications across diverse domains.However,their widespread deployment has introduced critical security vulnerabilities,particularly prompt injection attacks that manipulate model behavior through malicious instructions.Following Kitchenham’s guidelines,this systematic review synthesizes 128 peer-reviewed studies from 2022 to 2025 to provide a unified understanding of this rapidly evolving threat landscape.Our findings reveal a swift progression from simple direct injections to sophisticated multimodal attacks,achieving over 90%success rates against unprotected systems.In response,defense mechanisms show varying effectiveness:input preprocessing achieves 60%–80%detection rates and advanced architectural defenses demonstrate up to 95%protection against known patterns,though significant gaps persist against novel attack vectors.We identified 37 distinct defense approaches across three categories,but standardized evaluation frameworks remain limited.Our analysis attributes these vulnerabilities to fundamental LLM architectural limitations,such as the inability to distinguish instructions from data and attention mechanism vulnerabilities.This highlights critical research directions such as formal verification methods,standardized evaluation protocols,and architectural innovations for inherently secure LLM designs.
文摘Federated Learning(FL)enables joint training over distributed devices without data exchange but is highly vulnerable to attacks by adversaries in the form of model poisoning and malicious update injection.This work proposes Secured-FL,a blockchain-based defensive framework that combines smart contract-based authentication,clustering-driven outlier elimination,and dynamic threshold adjustment to defend against adversarial attacks.The framework was implemented on a private Ethereum network with a Proof-of-Authority consensus algorithm to ensure tamper-resistant and auditable model updates.Large-scale simulation on the Cyber Data dataset,under up to 50%malicious client settings,demonstrates Secured-FL achieves 6%-12%higher accuracy,9%-15%lower latency,and approximately 14%less computational expense compared to the PPSS benchmark framework.Additional tests,including confusion matrices,ROC and Precision-Recall curves,and ablation tests,confirm the interpretability and robustness of the defense.Tests for scalability also show consistent performance up to 500 clients,affirming appropriateness to reasonably large deployments.These results make Secured-FL a feasible,adversarially resilient FL paradigm with promising potential for application in smart cities,medicine,and other mission-critical IoT deployments.
基金supported by Punjab Agricultural University,Ludhiana,India,for providing the infrastructure and other facilities for conducting experiments.All other forms of support and financial assistance are duly acknowledged.
文摘Brown spot(BS)of rice,caused by Bipolaris oryzae,is a serious concern that not only causes quantitative losses but also affects grain quality.To manage this disease,the use of resistant genetic sources and QTLs is an eco-friendly and economical option.In the current study,F_(3) progenies derived from a cross of susceptible parent PMS-18-B(PAU 10845-1-1-1-1)×resistant parent RP Path 77(RP patho-17)were used to identify potential QTLs linked to BS resistance and to associate this resistance with a temporal spike in defense-related enzymes.
基金supported by the National Key Research and Development Program of China(No.2022YFB3404700)the National Natural Science Foundation of China(Nos.52105313 and 52275299)+2 种基金the Research and Development Program of Beijing Municipal Education Commission,China(No.KM202210005036)the Natural Science Foundation of Chongqing,China(No.CSTB2023NSCQ-MSX0701)the National Defense Basic Research Projects of China(No.JCKY2022405C002).
文摘At present,the emerging solid-phase friction-based additive manufacturing technology,including friction rolling additive man-ufacturing(FRAM),can only manufacture simple single-pass components.In this study,multi-layer multi-pass FRAM-deposited alumin-um alloy samples were successfully prepared using a non-shoulder tool head.The material flow behavior and microstructure of the over-lapped zone between adjacent layers and passes during multi-layer multi-pass FRAM deposition were studied using the hybrid 6061 and 5052 aluminum alloys.The results showed that a mechanical interlocking structure was formed between the adjacent layers and the adja-cent passes in the overlapped center area.Repeated friction and rolling of the tool head led to different degrees of lateral flow and plastic deformation of the materials in the overlapped zone,which made the recrystallization degree in the left and right edge zones of the over-lapped zone the highest,followed by the overlapped center zone and the non-overlapped zone.The tensile strength of the overlapped zone exceeded 90%of that of the single-pass deposition sample.It is proved that although there are uneven grooves on the surface of the over-lapping area during multi-layer and multi-pass deposition,they can be filled by the flow of materials during the deposition of the next lay-er,thus ensuring the dense microstructure and excellent mechanical properties of the overlapping area.The multi-layer multi-pass FRAM deposition overcomes the limitation of deposition width and lays the foundation for the future deposition of large-scale high-performance components.
基金supported jointly by the earmarked fund for CARS-10-GW2the key research and development program of Hainan Province(Grant No.ZDYF2020226)+1 种基金Collaborative innovation center of Nanfan and high-efficiency tropical agriculture,Hainan University(Grant No.XTCX2022NYC21)funding of Hainan University[Grant No.KYQD(ZR)22123]。
文摘Heat stress hinders the growth and productivity of sweetpotato plants,predominantly through oxidative damage to cellular membranes.Therefore,the development of efficient approaches for mitigating heat-related impairments is essential for the long-term production of sweetpotatoes.Melatonin has been recognised for its capacity to assist plants in dealing with abiotic stress conditions.This research aimed to investigate how different doses of exogenous melatonin influence heat damage in sweetpotato plants.Heat stress drastically affected shoot and root fresh weight by 31.8 and 44.5%,respectively.This reduction resulted in oxidative stress characterised by increased formation of hydrogen peroxide(H_(2)O_(2))by 804.4%,superoxide ion(O_(2)^(·-))by 211.5%and malondialdehyde(MDA)by 234.2%.Heat stress also reduced chlorophyll concentration,photosystemⅡefficiency(F_v/F_m)by 15.3%and gaseous exchange.However,pre-treatment with 100μmol L^(-1)melatonin increased growth and reduced oxidative damage to sweetpotato plants under heat stress.In particular,melatonin decreased H_(2)O_(2),O_(2)^(·-)and MDA by 64.8%,42.7%and 38.2%,respectively.Melatonin also mitigated the decline in chlorophyll levels and improved stomatal traits,gaseous exchange and F_(v)/F_(m)(13%).Results suggested that the favorable outcomes of melatonin treatment can be associated with elevated antioxidant enzyme activity and an increase in non-enzymatic antioxidants and osmo-protectants.Overall,these findings indicate that exogenous melatonin can improve heat stress tolerance in sweetpotatoes.This stu dy will assist re searchers in further investigating how melatonin makes sweetpotatoes more resistant to heat stress.
基金funded in part by the Humanities and Social Sciences Planning Foundation of Ministry of Education of China under Grant No.24YJAZH123National Undergraduate Innovation and Entrepreneurship Training Program of China under Grant No.202510347069the Huzhou Science and Technology Planning Foundation under Grant No.2023GZ04.
文摘The Industrial Internet of Things(IIoT)is increasingly vulnerable to sophisticated cyber threats,particularly zero-day attacks that exploit unknown vulnerabilities and evade traditional security measures.To address this critical challenge,this paper proposes a dynamic defense framework named Zero-day-aware Stackelberg Game-based Multi-Agent Distributed Deep Deterministic Policy Gradient(ZSG-MAD3PG).The framework integrates Stackelberg game modeling with the Multi-Agent Distributed Deep Deterministic Policy Gradient(MAD3PG)algorithm and incorporates defensive deception(DD)strategies to achieve adaptive and efficient protection.While conventional methods typically incur considerable resource overhead and exhibit higher latency due to static or rigid defensive mechanisms,the proposed ZSG-MAD3PG framework mitigates these limitations through multi-stage game modeling and adaptive learning,enabling more efficient resource utilization and faster response times.The Stackelberg-based architecture allows defenders to dynamically optimize packet sampling strategies,while attackers adjust their tactics to reach rapid equilibrium.Furthermore,dynamic deception techniques reduce the time required for the concealment of attacks and the overall system burden.A lightweight behavioral fingerprinting detection mechanism further enhances real-time zero-day attack identification within industrial device clusters.ZSG-MAD3PG demonstrates higher true positive rates(TPR)and lower false alarm rates(FAR)compared to existing methods,while also achieving improved latency,resource efficiency,and stealth adaptability in IIoT zero-day defense scenarios.
基金supported by the Science and Technology Development Program of Hebei Agricultural University,the Research on Molecular Mechanisms of Population Differentiation and Adaptation of Forest Pests and Insects under Environmental Stress(grant No.:30771739)Forest Pests and Diseases(grant No.:1528003)the National Natural Science Foundation of China for the study of community regulatory mechanisms of insect pest pandemics in larch plantation forests(Grant No.:32371882).
文摘The defense mechanisms induced in wild Chinese pine(Pinus tabuliformis)in response to herbivores are not well characterized,especially in the field.To address this knowledge gap,we established a biological model system to evaluate proteome variations in pine needles after feeding by the Chinese pine caterpillar(Dendrolimus tabulaeformis),a major natural enemy and dominant herbivore.Quantitative tandem mass tag(TMT)proteomics and bioinformatics were utilized to systematically identify differentially abundant proteins implicated in the induced defense response of Chinese pine.We validated key protein changes using parallel reaction monitoring(PRM)technology.Pathway analysis revealed that the induced defenses involved phenylpropanoid,coumarin,and flavonoid biosynthesis,among other processes.To elucidate the regulatory patterns underlying pine resistance,we determined the activities of defense enzymes and levels of physiological and biochemical compounds.In addition,the expression of upstream genes for key proteins was validated by qRT-PCR.Our results provide new molecular insights into the induced defense mechanisms in Chinese pine against this caterpillar in the field.A better understanding of these defense strategies will inform efforts to breed more-resistant pine varieties.
基金supported by the National Natural Science Foundation of China(No.32202963)the Natural Science Foundation of Jiangsu Province(No.BK20220681)+3 种基金the Doctoral Program of Entrepreneurship and Innovation in Jiangsu Province(No.JSSCBS20221625)the Scientific Research Foundation Program of Jiangsu Ocean University(No.KQ22009)the Undergraduate Innovation&Entrepreneurship Training Program of Jiangsu Province,China(No.SY202411641631001)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX2023-112).
文摘Due to the discharge of industrialwastewater,urban domestic sewage,and intensive marine aquaculture tailwater,nitrate(NO_(3)^(−))pollution has emerged as a significant issue in offshore waters.Nitrate pollution affects aquatic life and may interact with other pollutants,leading to comprehensive toxicity.Cadmium(Cd^(2+))is the most widespread metal contaminant,adversely affecting aquatic life in the coastal waters of China.Despite this,few studies have focused on the synergistic toxicity of NO_(3)^(−)and Cd^(2+)in marine organisms.This study conducted a 30-day exposure experiment on marine Japanese flounder(Paralichthys olivaceus)to explore the synergistic toxicity of NO_(3)^(−)and Cd^(2+).Our results demonstrated that the exposure to Cd^(2+)alone induced slight histopathological changes in the liver.However,malformations such as hepatic vacuolar degeneration and sinusoid dilatationwere exacerbated under co-exposure.Moreover,co-exposure induced the downregulation of antioxidants and the upregulation of the product malonaldehyde(MDA)from lipid peroxidation,indicating potent oxidative stress in the liver.The increased mRNA expression of IL-8,TNF-α,and IL-1β,along with the decreased expression level of TGF-β,indicated a synergistic inflammatory response in the organisms.Furthermore,the co-exposure led to an abnormal expression of P53,caspase-3,caspase-9,Bcl-2,and Bax,and disturbed the apoptosis in the liver through TUNEL staining analysis.Overall,our results imply that co-exposure synergistically affects inflammation,redox status,and apoptosis in flounders.Therefore,the findings from this study provide valuable perspectives on the ecological risk assessment of marine teleosts co-exposure to NO_(3)^(−)and Cd^(2+).
基金supported by the Excellent Youth Science Project of Henan Natural Science Foundation(242300421110)the National Natural Science Foundation of China(32372129,32272038)Henan Provincial Nature Foundation Project(242300420151).
文摘Benzoxazinoids(BXDs)are a class of plant secondary metabolites that play pivotal roles in plant defense against pathogens and pests,as well as in allelopathy.This review synthesizes recent advances in our understanding of the structural and functional diversity of BXDs,the independent evolutionary trajectories of their biosynthetic pathways across different plant species,their metabolic transformations in target organisms,and the opportunities and challenges of optimizing BXD biosynthesis in crops through metabolic engineering.Compared with monocotyledons,dicotyledons employ a more diverse set of enzymes to catalyze the core reactions of BXD biosynthesis.This functional divergence—yet biochemical convergence—between monocotyledons and dicotyledons exemplifies the convergent evolution of BXD biosynthetic pathways in plants.BXDs act not only as potent antifeedants,insecticides,and antimicrobials but also function as signaling molecules that induce callose deposition and activate systemic immunity,thereby enhancing plant resistance to biotic stress.Furthermore,BXDs shape the rhizosphere by modulating microbial communities through species-specific antimicrobial activities and microbial detoxification mechanisms,ultimately exerting allelopathic effects that alter soil chemistry and nutrient dynamics.The translational potential of BXDs is increasingly recognized by synthetic biology approaches,including artificial intelligence-driven enzyme optimization,heterologous pathway engineering,and gene-editing to enhance crop resistance.Despite these promising prospects,challenges remain in balancing metabolic trade-offs and mitigating ecological risks associated with persistent accumulation of BXDs.Future research integrating multi-omics,evolutionary genomics,and microbiome studies will be essential to fully harness BXDs for sustainable crop improvement and reduced reliance on synthetic agrochemicals.
文摘This paper investigates the number of limit cycles in a predator-prey system with group defense,intially introduced by Wolkowicz and later examined by Rothe and Shafer in the 1980’s.Under the assumption of large prey growth,the system reduces to a perturbed singular system,whose limit cycles can be analyzed using geometric singular perturbation methods-primarily through the study of a slow-divergence integral.Our work completes partially the results previously obtained by Li and Zhu and by Hsu.We provide a comprehensive classification of all possible singular cycles capable of generating limit cycles and analyze the slow-divergence integral for the nine distinct types of cycle families that arise in a canard explosion.Based on these findings,we demonstrate that the maximum number of limit cycles emerging from the singular cycles is two in all cases,thereby confirming conjectures posed by Rothe-Shafer and Xiao-Ruan.
基金Supported by the earmarked fund for China Agriculture Research System(No.CARS-50)the Doctors Research Funding of Henan Normal University(No.20230246)。
文摘Nanoplastics(less than 1µm in size,NPs)have emerged as a significant pollutant in aquatic environment,posing considerable threats to freshwater biota.However,the mechanisms through which NPs modulate the predation responses of these organisms remain poorly elucidated.We investigated the impacts of polystyrene NPs,characterized by a representative particle size(diameter:50 nm;concentration:0–8μg/L),on the anti-predation defense mechanisms of mature rotifer Brachionus calyciflorus against predator of rotifer Asplanchna brightwellii,utilizing transcriptomics to unravel the underlying molecular pathways.Results reveal that the posterolateral spine length and type of B.calyciflorus serve as robust indicators of defensive morphology,even in the presence of NPs exposure.Specifically,increasing concentrations of NPs and predator cues suppressed the defensive responses,which was associated with morphological transformations.This suppression was associated with the down-regulation of the HIF-1αsignaling pathway,implicating potentially its role in modulating fight-or-flight responses.Furthermore,we identified functional crosstalk among multiple signaling pathways,including HIF-1α,PI3K-Akt,FoxO,and mTOR,in B.calyciflorus,which may underpin the organism's responses to polystyrene NP exposure.These findings contribute to the advancement of predictive models to assess the ecological risks posed by polystyrene NPs contamination in aquatic ecosystems.
基金Supported by the National Natural Science Foundation of China(U1903214,62372339,62371350,61876135)the Ministry of Education Industry University Cooperative Education Project(202102246004,220800006041043,202002142012)the Fundamental Research Funds for the Central Universities(2042023kf1033)。
文摘Recent years have witnessed the ever-increasing performance of Deep Neural Networks(DNNs)in computer vision tasks.However,researchers have identified a potential vulnerability:carefully crafted adversarial examples can easily mislead DNNs into incorrect behavior via the injection of imperceptible modification to the input data.In this survey,we focus on(1)adversarial attack algorithms to generate adversarial examples,(2)adversarial defense techniques to secure DNNs against adversarial examples,and(3)important problems in the realm of adversarial examples beyond attack and defense,including the theoretical explanations,trade-off issues and benign attacks in adversarial examples.Additionally,we draw a brief comparison between recently published surveys on adversarial examples,and identify the future directions for the research of adversarial examples,such as the generalization of methods and the understanding of transferability,that might be solutions to the open problems in this field.
基金funded by the National Natural Science Foundation of China Project"Research on Intelligent Detection Techniques of Encrypted Malicious Traffic for Large-Scale Networks"(Grant No.62176264).
文摘Deep neural networks are known to be vulnerable to adversarial attacks.Unfortunately,the underlying mechanisms remain insufficiently understood,leading to empirical defenses that often fail against new attacks.In this paper,we explain adversarial attacks from the perspective of robust features,and propose a novel Generative Adversarial Network(GAN)-based Robust Feature Disentanglement framework(GRFD)for adversarial defense.The core of GRFD is an adversarial disentanglement structure comprising a generator and a discriminator.For the generator,we introduce a novel Latent Variable Constrained Variational Auto-Encoder(LVCVAE),which enhances the typical beta-VAE with a constrained rectification module to enforce explicit clustering of latent variables.To supervise the disentanglement of robust features,we design a Robust Supervisory Model(RSM)as the discriminator,sharing architectural alignment with the target model.The key innovation of RSM is our proposed Feature Robustness Metric(FRM),which serves as part of the training loss and synthesizes the classification ability of features as well as their resistance to perturbations.Extensive experiments on three benchmark datasets demonstrate the superiority of GRFD:it achieves 93.69%adversarial accuracy on MNIST,77.21%on CIFAR10,and 58.91%on CIFAR100 with minimal degradation in clean accuracy.Codes are available at:(accessed on 23 July 2025).
基金Supported by the National Natural Science Foundation of China(Grant No.52172409)Postdoctoral Innovation Talents Support Program(Grant No.BX20240298)+1 种基金the Fundamental Research Funds for the Central Universities(Grant No.2682024GF023)Heilongjiang Province Postdoctoral Foundation Project(Grant No.LBH-Z23041).
文摘The huge impact kinetic energy cannot be quickly dissipated by the energy-absorbing structure and transferred to the other vehicle through the car body structure,which will cause structural damage and threaten the lives of the occupants.Therefore,it is necessary to understand the laws of energy conversion,dissipation and transfer during train collisions.This study proposes a multi-layer progressive analysis method of energy flow during train collisions,considering the characteristics of the train.In this method,the train collision system is divided into conversion,dissipation,and transfer layers from the perspective of the train,collision interface,and car body structure to analyze the energy conversion,dissipation and transfer characteristics.Taking the collision process of a rail train as an example,a train collision energy transfer path analysis model was established based on power flow theory.The results show that when the maximum mean acceleration of the vehicle meets the standard requirements,the jerk may exceed the allowable limit of the human body,and there is a risk of injury to the occupants of a secondary collision.The decay rate of the collision energy along the direction of train operation reaches 79%.As the collision progresses,the collision energy gradually converges in the structure with holes,and the structure deforms when the gathered energy is greater than the maximum energy the structure can withstand.The proposed method helps to understand the train collision energy flow law and provides theoretical support for the train crashworthiness design in the future.
文摘Aiming at the terminal defense problem of aircraft,this paper proposes a method to simultaneously achieve terminal defense and seize the dominant position.The method employs aλ-return based reinforcement learning algorithm,which can be applied to the flight assistance decision-making system to improve the pilot’s survivability.First,we model the environment to simulate the interaction between air-to-air missiles and aircraft.Subsequently,we propose aλ-return based approach to improve the deep Q learning network(DQN),deep advantageous actor criticism(A2C),and proximity policy optimization(PPO)algorithms used to train manoeuvre strategies.The method employs an action space containing nine manoeuvres and defines the off-target distance at the end of the scene as a sparse reward for algorithm training.Simulation results show that the convergence speed of the three improved algorithms is significantly improved when using theλ-return method.Moreover,the effect of the fetch value on the convergence speed is verified by ablation experiments.In order to solve the illegal behavior problem in the training process,we also design a backtracking-based illegal behavior masking mechanism,which improves the data generation efficiency of the environment model and promotes effective algorithm training.
基金Nourah bint Abdulrahman University for funding this project through the Researchers Supporting Project(PNURSP2025R319)Riyadh,Saudi Arabia and Prince Sultan University for covering the article processing charges(APC)associated with this publication.Special acknowledgement to Automated Systems&Soft Computing Lab(ASSCL),Prince Sultan University,Riyadh,Saudi Arabia.
文摘The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems(IDS)for enhanced network security.While conventional IDSs can be unsuitable for detecting different and emerging attacks,there is a demand for better techniques to improve detection reliability.This study introduces a new method,the Deep Adaptive Multi-Layer Attention Network(DAMLAN),to boost the result of intrusion detection on network data.Due to its multi-scale attention mechanisms and graph features,DAMLAN aims to address both known and unknown intrusions.The real-world NSL-KDD dataset,a popular choice among IDS researchers,is used to assess the proposed model.There are 67,343 normal samples and 58,630 intrusion attacks in the training set,12,833 normal samples,and 9711 intrusion attacks in the test set.Thus,the proposed DAMLAN method is more effective than the standard models due to the consideration of patterns by the attention layers.The experimental performance of the proposed model demonstrates that it achieves 99.26%training accuracy and 90.68%testing accuracy,with precision reaching 98.54%on the training set and 96.64%on the testing set.The recall and F1 scores again support the model with training set values of 99.90%and 99.21%and testing set values of 86.65%and 91.37%.These results provide a strong basis for the claims made regarding the model’s potential to identify intrusion attacks and affirm its relatively strong overall performance,irrespective of type.Future work would employ more attempts to extend the scalability and applicability of DAMLAN for real-time use in intrusion detection systems.
文摘Unlike most plants, members of the genus Solanum produce cholesterol and use this as a precursor for steroidal glycoalkaloids. The production of the compounds begins as a branch from brassinosteroid biosynthesis, which produces cholesterol that is further modified to produce steroidal glycoalkaloids. During the cholesterol biosynthesis pathway, genetic engineering could alter the formation of cholesterol from provitamin D3(7-dehydrocholesterol) and produce vitamin D3. Cholesterol is a precursor for many steroidal glycoalkaloids, including a-tomatine and esculeoside A. Alpha-tomatine is consumed by mammals and it can reduce cholesterol content and improve LDL:HDL ratio. When there is a high a-tomatine content, the fruit will have a bitter flavor, which together with other steroidal glycoalkaloids serving as protective and defensive compounds for tomato against insect, fungal, and bacterial pests. These compounds also affect the rhizosphere bacteria by recruiting beneficial bacteria. One of the steroidal glycoalkaloids, esculeoside A increases while fruit ripening. This review focuses on recent studies that uncovered key reactions of the production of cholesterol and steroidal glycoalkaloids in tomato connecting to human health, fruit flavor, and plant defense and the potential application for tomato crop improvement.
基金Project(2023YFC3707800)supported by the National Key Research and Development Program of China。
文摘In practical engineering construction,multi-layered barriers containing geomembranes are extensively applied to retard the migration of pollutants.However,the associated analytical theory on pollutants diffusion still needs to be further improved.In this work,general analytical solutions are derived for one-dimensional diffusion of degradable organic contaminant(DOC)in the multi-layered media containing geomembranes under a time-varying concentration boundary condition,where the variable substitution and separated variable approaches are employed.These analytical solutions with clear expressions can be used not only to study the diffusion behaviors of DOC in bottom and vertical composite barrier systems,but also to verify other complex numerical models.The proposed general analytical solutions are then fully validated via three comparative analyses,including comparisons with the experimental measurements,an existing analytical solution,and a finite-difference solution.Ultimately,the influences of different factors on the composite cutoff wall’s(CCW,which consists of two soil-bentonite layers and a geomembrane)service performance are investigated through a composite vertical barrier system as the application example.The findings obtained from this investigation can provide scientific guidance for the barrier performance evaluation and the engineering design of CCWs.This application example also exhibits the necessity and effectiveness of the developed analytical solutions.
