This paper explores the issue of secure synchronization control in piecewise-homogeneous Markovian jump delay neural networks affected by denial-of-service(DoS)attacks.Initially,a novel memory-based adaptive event-tri...This paper explores the issue of secure synchronization control in piecewise-homogeneous Markovian jump delay neural networks affected by denial-of-service(DoS)attacks.Initially,a novel memory-based adaptive event-triggered mechanism(MBAETM)is designed based on sequential growth rates,focusing on event-triggered conditions and thresholds.Subsequently,from the perspective of defenders,non-periodic DoS attacks are re-characterized,and a model of irregular DoS attacks with cyclic fluctuations within time series is further introduced to enhance the system's defense capabilities more effectively.Additionally,considering the unified demands of network security and communication efficiency,a resilient memory-based adaptive event-triggered mechanism(RMBAETM)is proposed.A unified Lyapunov-Krasovskii functional is then constructed,incorporating a loop functional to thoroughly consider information at trigger moments.The master-slave system achieves synchronization through the application of linear matrix inequality techniques.Finally,the proposed methods'effectiveness and superiority are confirmed through four numerical simulation examples.展开更多
Developmental and reproductive toxicity(DART)endpoint entails a toxicological assessment of all developmental stages and reproductive cycles of an organism.In silico tools to predict DART will provide a method to asse...Developmental and reproductive toxicity(DART)endpoint entails a toxicological assessment of all developmental stages and reproductive cycles of an organism.In silico tools to predict DART will provide a method to assess this complex toxicity endpoint and will be valuable for screening emerging pollutants as well as for m anaging new chemicals in China.Currently,there are few published DART prediction models in China,but many related research and development projects are in progress.In 2013,WU et al.published an expert rule-based DART decision tree(DT).This DT relies on known chemical structures linked to DART to forecast DART potential of a given chemical.Within this procedure,an accurate DART data interpretation is the foundation of building and expanding the DT.This paper excerpted case studies demonstrating DART data curation and interpretation of four chemicals(including 8-hydroxyquinoline,3,5,6-trichloro-2-pyridinol,thiacloprid,and imidacloprid)to expand the existing DART DT.Chemicals were first selected from the database of Solid Waste and Chemicals Management Center,Ministry of Ecology and Environment(MEESCC)in China.The structures of these 4 chemicals were analyzed and preliminarily grouped by chemists based on core structural features,functional groups,receptor binding property,metabolism,and possible mode of actions.Then,the DART conclusion was derived by collecting chemical information,searching,integrating,and interpreting DART data by the toxicologists.Finally,these chemicals were classified into either an existing category or a new category via integrating their chemical features,DART conclusions,and biological properties.The results showed that 8-hydroxyquinoline impacted estrous cyclicity,s exual organ weights,and embryonal development,and 3,5,6-trichloro-2-pyridinol caused central nervous system(CNS)malformations,which were added to an existing subcategory 8e(aromatic compounds with multi-halogen and nitro groups)of the DT.Thiacloprid caused dystocia and fetal skeletal malformation,and imidacloprid disrupted the endocrine system and male fertility.They both contain 2-chloro-5-methylpyridine substituted imidazolidine c yclic ring,which were expected to create a new category of neonicotinoids.The current work delineates a t ransparent process of curating toxicological data for the purpose of DART data interpretation.In the presence of sufficient related structures and DART data,the DT can be expanded by iteratively adding chemicals within the a pplicable domain of each category or subcategory.This DT can potentially serve as a tool for screening emerging pollutants and assessing new chemicals in China.展开更多
Advancements in Natural Language Processing and Deep Learning techniques have significantly pro-pelled the automation of Legal Judgment Prediction,achieving remarkable progress in legal research.Most of the existing r...Advancements in Natural Language Processing and Deep Learning techniques have significantly pro-pelled the automation of Legal Judgment Prediction,achieving remarkable progress in legal research.Most of the existing research works on Legal Judgment Prediction(LJP)use traditional optimization algorithms in deep learning techniques falling into local optimization.This research article focuses on using the modified Pelican Optimization method which mimics the collective behavior of Pelicans in the exploration and exploitation phase during cooperative food searching.Typically,the selection of search agents within a boundary is done randomly,which increases the time required to achieve global optimization.To address this,the proposed Chaotic Opposition Learning-based Pelican Optimization(COLPO)method incorporates the concept of Opposition-Based Learning combined with a chaotic cubic function,enabling deterministic selection of random numbers and reducing the number of iterations needed to reach global optimization.Also,the LJP approach in this work uses improved semantic similarity and entropy features to train a hybrid classifier combining Bi-GRU and Deep Maxout.The output scores are fused using improved score level fusion to boost prediction accuracy.The proposed COLPO method experiments with real-time Madras High Court criminal cases(Dataset 1)and the Supreme Court of India database(Dataset 2),and its performance is compared with nature-inspired algorithms such as Sparrow Search Algorithm(SSA),COOT,Spider Monkey Optimization(SMO),Pelican Optimization Algorithm(POA),as well as baseline classifier models and transformer neural networks.The results show that the proposed hybrid classifier with COLPO outperforms other cutting-edge LJP algorithms achieving 93.4%and 94.24%accuracy,respectively.展开更多
This article utilizes Katherine Mansfield’s short story The Garden Party as the research object to explore the narrative generation conditions of ethical experience in the text. Through a close analysis of the novel...This article utilizes Katherine Mansfield’s short story The Garden Party as the research object to explore the narrative generation conditions of ethical experience in the text. Through a close analysis of the novel’s narrative structure and key scenes, the article argues that ethical discomfort does not evolve into enduring moral judgments within the text;rather, it is continually managed and deferred through the interplay of aesthetic order, familial discourse, and the distribution of social roles. The novel eschews a linear trajectory of ethical awakening, instead crafting a narrative mechanism that keeps ethical experience palpable yet inarticulable. The female subject is given the role of sensing ethical incongruity, but lacks the narrative position from which to articulate it as judgment. Consequently, ethics remains confined to the level of personalization and unimplementability. Far from a narrative of moral growth or awakening, The Garden Party exposes why ethical judgment has become structurally unrealizable in modern narratives.展开更多
This paper studies the challenging problem of model-free adaptive(MFA)security tracking control for nonlinear multi-agent systems(MASs)under mixed denial-of-service(DoS)attacks.First,in contrast to existing results fo...This paper studies the challenging problem of model-free adaptive(MFA)security tracking control for nonlinear multi-agent systems(MASs)under mixed denial-of-service(DoS)attacks.First,in contrast to existing results focusing on DoS attacks with one monotonic characteristic only,a more realistic mixed DoS attacks model is constructed,which can describe multiple types of DoS attacks and reflect the real attack strategy.Second,to mitigate the negative effect of mixed DoS attacks on control performance,an effective memory event-triggered mechanism is considered.Compared with existing event-triggered schemes,the developed memory event-triggered scheme utilizes historically triggered data and allows the released data to adjust adaptively using the long-term changes of the system state,which optimizes the utilization of communication resources and withstands the effect of mixed DoS attacks.Further,with the help of a dynamic linearization technique based on memory eventtriggered strategy,a linearized data model of the MASs is first established only depending on input/output information.Then,an improved memory event-triggered MFA security tracking control scheme is developed so that MASs can guarantee the tracking errors of all agents are bounded under mixed DoS attacks.Finally,a simulation example is presented of the designed MFA security tracking control method to illustrate its usefulness and advantages.展开更多
文摘This paper explores the issue of secure synchronization control in piecewise-homogeneous Markovian jump delay neural networks affected by denial-of-service(DoS)attacks.Initially,a novel memory-based adaptive event-triggered mechanism(MBAETM)is designed based on sequential growth rates,focusing on event-triggered conditions and thresholds.Subsequently,from the perspective of defenders,non-periodic DoS attacks are re-characterized,and a model of irregular DoS attacks with cyclic fluctuations within time series is further introduced to enhance the system's defense capabilities more effectively.Additionally,considering the unified demands of network security and communication efficiency,a resilient memory-based adaptive event-triggered mechanism(RMBAETM)is proposed.A unified Lyapunov-Krasovskii functional is then constructed,incorporating a loop functional to thoroughly consider information at trigger moments.The master-slave system achieves synchronization through the application of linear matrix inequality techniques.Finally,the proposed methods'effectiveness and superiority are confirmed through four numerical simulation examples.
文摘Developmental and reproductive toxicity(DART)endpoint entails a toxicological assessment of all developmental stages and reproductive cycles of an organism.In silico tools to predict DART will provide a method to assess this complex toxicity endpoint and will be valuable for screening emerging pollutants as well as for m anaging new chemicals in China.Currently,there are few published DART prediction models in China,but many related research and development projects are in progress.In 2013,WU et al.published an expert rule-based DART decision tree(DT).This DT relies on known chemical structures linked to DART to forecast DART potential of a given chemical.Within this procedure,an accurate DART data interpretation is the foundation of building and expanding the DT.This paper excerpted case studies demonstrating DART data curation and interpretation of four chemicals(including 8-hydroxyquinoline,3,5,6-trichloro-2-pyridinol,thiacloprid,and imidacloprid)to expand the existing DART DT.Chemicals were first selected from the database of Solid Waste and Chemicals Management Center,Ministry of Ecology and Environment(MEESCC)in China.The structures of these 4 chemicals were analyzed and preliminarily grouped by chemists based on core structural features,functional groups,receptor binding property,metabolism,and possible mode of actions.Then,the DART conclusion was derived by collecting chemical information,searching,integrating,and interpreting DART data by the toxicologists.Finally,these chemicals were classified into either an existing category or a new category via integrating their chemical features,DART conclusions,and biological properties.The results showed that 8-hydroxyquinoline impacted estrous cyclicity,s exual organ weights,and embryonal development,and 3,5,6-trichloro-2-pyridinol caused central nervous system(CNS)malformations,which were added to an existing subcategory 8e(aromatic compounds with multi-halogen and nitro groups)of the DT.Thiacloprid caused dystocia and fetal skeletal malformation,and imidacloprid disrupted the endocrine system and male fertility.They both contain 2-chloro-5-methylpyridine substituted imidazolidine c yclic ring,which were expected to create a new category of neonicotinoids.The current work delineates a t ransparent process of curating toxicological data for the purpose of DART data interpretation.In the presence of sufficient related structures and DART data,the DT can be expanded by iteratively adding chemicals within the a pplicable domain of each category or subcategory.This DT can potentially serve as a tool for screening emerging pollutants and assessing new chemicals in China.
文摘Advancements in Natural Language Processing and Deep Learning techniques have significantly pro-pelled the automation of Legal Judgment Prediction,achieving remarkable progress in legal research.Most of the existing research works on Legal Judgment Prediction(LJP)use traditional optimization algorithms in deep learning techniques falling into local optimization.This research article focuses on using the modified Pelican Optimization method which mimics the collective behavior of Pelicans in the exploration and exploitation phase during cooperative food searching.Typically,the selection of search agents within a boundary is done randomly,which increases the time required to achieve global optimization.To address this,the proposed Chaotic Opposition Learning-based Pelican Optimization(COLPO)method incorporates the concept of Opposition-Based Learning combined with a chaotic cubic function,enabling deterministic selection of random numbers and reducing the number of iterations needed to reach global optimization.Also,the LJP approach in this work uses improved semantic similarity and entropy features to train a hybrid classifier combining Bi-GRU and Deep Maxout.The output scores are fused using improved score level fusion to boost prediction accuracy.The proposed COLPO method experiments with real-time Madras High Court criminal cases(Dataset 1)and the Supreme Court of India database(Dataset 2),and its performance is compared with nature-inspired algorithms such as Sparrow Search Algorithm(SSA),COOT,Spider Monkey Optimization(SMO),Pelican Optimization Algorithm(POA),as well as baseline classifier models and transformer neural networks.The results show that the proposed hybrid classifier with COLPO outperforms other cutting-edge LJP algorithms achieving 93.4%and 94.24%accuracy,respectively.
文摘This article utilizes Katherine Mansfield’s short story The Garden Party as the research object to explore the narrative generation conditions of ethical experience in the text. Through a close analysis of the novel’s narrative structure and key scenes, the article argues that ethical discomfort does not evolve into enduring moral judgments within the text;rather, it is continually managed and deferred through the interplay of aesthetic order, familial discourse, and the distribution of social roles. The novel eschews a linear trajectory of ethical awakening, instead crafting a narrative mechanism that keeps ethical experience palpable yet inarticulable. The female subject is given the role of sensing ethical incongruity, but lacks the narrative position from which to articulate it as judgment. Consequently, ethics remains confined to the level of personalization and unimplementability. Far from a narrative of moral growth or awakening, The Garden Party exposes why ethical judgment has become structurally unrealizable in modern narratives.
基金supported by the National Natural Science Foundation of China(Grant Nos.62303125,62373113,62433018,62433014,62303121,62373111)the Guangdong Basic and Applied Basic Research Foundation(Grant Nos.2022A1515110949,2023A1515011527,2023B1515120010,2025A1515011343,2023A1515010855)the Science and Technology Planning Project of Guangzhou City(Grant No.2025A04J4336)。
文摘This paper studies the challenging problem of model-free adaptive(MFA)security tracking control for nonlinear multi-agent systems(MASs)under mixed denial-of-service(DoS)attacks.First,in contrast to existing results focusing on DoS attacks with one monotonic characteristic only,a more realistic mixed DoS attacks model is constructed,which can describe multiple types of DoS attacks and reflect the real attack strategy.Second,to mitigate the negative effect of mixed DoS attacks on control performance,an effective memory event-triggered mechanism is considered.Compared with existing event-triggered schemes,the developed memory event-triggered scheme utilizes historically triggered data and allows the released data to adjust adaptively using the long-term changes of the system state,which optimizes the utilization of communication resources and withstands the effect of mixed DoS attacks.Further,with the help of a dynamic linearization technique based on memory eventtriggered strategy,a linearized data model of the MASs is first established only depending on input/output information.Then,an improved memory event-triggered MFA security tracking control scheme is developed so that MASs can guarantee the tracking errors of all agents are bounded under mixed DoS attacks.Finally,a simulation example is presented of the designed MFA security tracking control method to illustrate its usefulness and advantages.
