During the storage of composite propellants, the migration of plasticizers and other unbonded additives at the interfaces of liner adhesives has garnered significant attention in understanding liner failure mechanisms...During the storage of composite propellants, the migration of plasticizers and other unbonded additives at the interfaces of liner adhesives has garnered significant attention in understanding liner failure mechanisms, aging processes, and safety performance. However, there is currently no non-destructive and quantitative detection method for migration of plasticizers in propellant liner. In this study, we developed a HTPB sensing liner by incorporating conductive fillers-namely carbon black(CB), carbon nanotubes(CNTs), and graphene nanoplatelets(GNP)-into the HTPB matrix. The synergistic interaction between CNTs and GNP facilitates the formation of a tunneling conductive network that imparts electrical conductivity to the HTPB liner. To elucidate the functional relationship between conductivity and nitroglycerin(NG) migration, we applied the HTPB sensing liner onto double base propellant surfaces and measured both the conductivity of the sensing layer and NG migration during a 71°C accelerated aging experiment. The results shows that when CNTs/GNP content reaches 3wt%, there is an exponential correlation between conductivity and NG migration with a fitting degree of 0.9652;the average response sensitivity of ΔR/R0 relative to NG migration is calculated as 41.69, with an average deviation of merely5.67% between NG migrations derived from conductivity fittings compared to those obtained via TGA testing results. Overall, this sensing liner exhibits excellent capabilities for detecting NG migration nondestructively and quantitatively while offering a novel approach for assessing interfacial component migrations as well as debonding defects in propellants-a promising avenue for future self-monitoring strategies regarding propellant integrity.展开更多
The dividing wall column （DWC） is considered as a major breakthrough in distillation technology and has good prospect of industrialization. Model predictive control （MPC） is an advanced control strategy that has a...The dividing wall column （DWC） is considered as a major breakthrough in distillation technology and has good prospect of industrialization. Model predictive control （MPC） is an advanced control strategy that has acquired extensive applications in various industries. In this study, MPC is applied to the process for separating ethanol, n-propanol, and n-butanol ternary mixture in a fully thermally coupled DWC. Both composition control and tem- perature inferent/al control are considered. The multiobjective genetic algor/thm function ＂gamult/obj＂ in Matlab is used for the weight tuning of MPC. Comparisons are made between the control performances of MPC and PI strategies. Simulation results show that although both MPC and PI schemes can stabilize the DWC in case of feed disturbances, MPC generally behaves better than the PI strategy for both composition control and tempera- ture inferential control, resulting in a more stable and superior performance with lower values of integral of squared error （ISE）.展开更多
In this paper, the mixture of dimethyl carbonate, ethyl methyl carbonate and diethyl carbonate was separated by middle-vessel batch distillation with feeding in middle-vessel and process control characteristics were r...In this paper, the mixture of dimethyl carbonate, ethyl methyl carbonate and diethyl carbonate was separated by middle-vessel batch distillation with feeding in middle-vessel and process control characteristics were researched. The steady state simulation results in Aspen Plus were exported to Aspen Dynamics. Then control effect of liquid level control with HighSelector, composition control(structure1, structure2) and temperature control(proportional action, proportional integration action) were proposed. Composition control structure 2 and temperature control with PI action were investigated to achieve a good control effect.展开更多
Nowadays, machine learning is widely used in malware detection system as a core component. The machine learning algorithm is designed under the assumption that all datasets follow the same underlying data distribution...Nowadays, machine learning is widely used in malware detection system as a core component. The machine learning algorithm is designed under the assumption that all datasets follow the same underlying data distribution. But the real-world malware data distribution is not stable and changes with time. By exploiting the knowledge of the machine learning algorithm and malware data concept drift problem, we show a novel learning evasive botnet architecture and a stealthy and secure C&C mechanism. Based on the email communication channel, we construct a stealthy email-based P2 P-like botnet that exploit the excellent reputation of email servers and a huge amount of benign email communication in the same channel. The experiment results show horizontal correlation learning algorithm is difficult to separate malicious email traffic from normal email traffic based on the volume features and time-related features with enough confidence. We discuss the malware data concept drift and possible defense strategies.展开更多
Persuasion,as one of the crucial abilities in human communication,has garnered extensive attention from researchers within the field of intelligent dialogue systems.Developing dialogue agents that can persuade others ...Persuasion,as one of the crucial abilities in human communication,has garnered extensive attention from researchers within the field of intelligent dialogue systems.Developing dialogue agents that can persuade others to accept certain standpoints is essential to achieving truly intelligent and anthropomorphic dialogue systems.Benefiting from the substantial progress of Large Language Models(LLMs),dialogue agents have acquired an exceptional capability in context understanding and response generation.However,as a typical and complicated cognitive psychological system,persuasive dialogue agents also require knowledge from the domain of cognitive psychology to attain a level of human-like persuasion.Consequently,the cognitive strategy-enhanced persuasive dialogue agent(defined as CogAgent),which incorporates cognitive strategies to achieve persuasive targets through conversation,has become a predominant research paradigm.To depict the research trends of CogAgent,in this paper,we first present several fundamental cognitive psychology theories and give the formalized definition of three typical cognitive strategies,including the persuasion strategy,the topic path planning strategy,and the argument structure prediction strategy.Then we propose a new system architecture by incorporating the formalized definition to lay the foundation of CogAgent.Representative works are detailed and investigated according to the combined cognitive strategy,followed by the summary of authoritative benchmarks and evaluation metrics.Finally,we summarize our insights on open issues and future directions of CogAgent for upcoming researchers.展开更多
Video-Grounded Dialogue System(VGDS),focusing on generating reasonable responses based on multiturn dialogue contexts and a given video,has received intensive attention recently.The key to building a superior VGDS lie...Video-Grounded Dialogue System(VGDS),focusing on generating reasonable responses based on multiturn dialogue contexts and a given video,has received intensive attention recently.The key to building a superior VGDS lies in efficiently reasoning over visual and textual concepts of various granularities and achieving comprehensive visual-textual multimodality alignment.Despite remarkable research progress,existing studies suffer from identifying context-relevant video parts while disregarding the impact of redundant information in long-form and content-dynamic videos.Further,current methods usually align all semantics in different modalities uniformly using a one-time cross-attention scheme,which neglects the sophisticated correspondence between various granularities of visual and textual concepts(e.g.,still objects with nouns,dynamic events with verbs).To this end,we propose a novel system,namely Cascade cOntext-oriented Spatio-Temporal Attention Network(COSTA),to generate reasonable responses efficiently and accurately.Specifically,COSTA first adopts a cascade attention network to localize only the most relevant video clips and regions in a coarse-tofine manner which effectively filters the irrelevant visual semantics.Secondly,we design a memory distillation-inspired iterative visual-textual cross-attention strategy to progressively integrate visual semantics with dialogue contexts across varying granularities,facilitating extensive multi-modal alignment.Experiments on several benchmarks demonstrate significant improvements in our model over state-of-the-art methods across various metrics.展开更多
Acute severe lower gastrointestinal bleeding is a rare but potentially fatal complication of Crohn's disease(CD),affecting between 0.6%and 5.5%of CD patients during their lifelong disease course.Managing bleeding ...Acute severe lower gastrointestinal bleeding is a rare but potentially fatal complication of Crohn's disease(CD),affecting between 0.6%and 5.5%of CD patients during their lifelong disease course.Managing bleeding episodes effectively hinges on vital resuscitation.Endoscopic evaluation and computed tomography play crucial roles in accurate identification and intervention.Fortunately,most bleeding episodes can be successfully managed through appropriate conservative treatment.Medical therapies,particularly infliximab,aim to induce and maintain mucosal healing and serve as the leading treatment approach.Minimally invasive procedures,such as endoscopic hemostasis and angio-embolization,can achieve immediate hemostasis.Surgical treatment is only considered a last resort when conservative therapies fail.Despite achieving hemostasis,the risk of rebleeding ranges from 19.0%to 50.5%.The objective of this review is to provide a comprehensive and updated overview of the clinical manifestations,diagnostic methods,therapeutic approaches,and prognostic outcomes associated with acute severe gastrointestinal bleeding in CD.Furthermore,we aimed to propose a management algorithm to assist clinicians in the effective management of this condition.展开更多
基金funded by Zhijian Laboratory Open Fund,Rocket Force University of Engineering(Grant No.2023-ZJSYS-KF01-03).
文摘During the storage of composite propellants, the migration of plasticizers and other unbonded additives at the interfaces of liner adhesives has garnered significant attention in understanding liner failure mechanisms, aging processes, and safety performance. However, there is currently no non-destructive and quantitative detection method for migration of plasticizers in propellant liner. In this study, we developed a HTPB sensing liner by incorporating conductive fillers-namely carbon black(CB), carbon nanotubes(CNTs), and graphene nanoplatelets(GNP)-into the HTPB matrix. The synergistic interaction between CNTs and GNP facilitates the formation of a tunneling conductive network that imparts electrical conductivity to the HTPB liner. To elucidate the functional relationship between conductivity and nitroglycerin(NG) migration, we applied the HTPB sensing liner onto double base propellant surfaces and measured both the conductivity of the sensing layer and NG migration during a 71°C accelerated aging experiment. The results shows that when CNTs/GNP content reaches 3wt%, there is an exponential correlation between conductivity and NG migration with a fitting degree of 0.9652;the average response sensitivity of ΔR/R0 relative to NG migration is calculated as 41.69, with an average deviation of merely5.67% between NG migrations derived from conductivity fittings compared to those obtained via TGA testing results. Overall, this sensing liner exhibits excellent capabilities for detecting NG migration nondestructively and quantitatively while offering a novel approach for assessing interfacial component migrations as well as debonding defects in propellants-a promising avenue for future self-monitoring strategies regarding propellant integrity.
基金Supported by the National Natural Science Foundation of China(21676299,21476261and 21606255)
文摘The dividing wall column （DWC） is considered as a major breakthrough in distillation technology and has good prospect of industrialization. Model predictive control （MPC） is an advanced control strategy that has acquired extensive applications in various industries. In this study, MPC is applied to the process for separating ethanol, n-propanol, and n-butanol ternary mixture in a fully thermally coupled DWC. Both composition control and tem- perature inferent/al control are considered. The multiobjective genetic algor/thm function ＂gamult/obj＂ in Matlab is used for the weight tuning of MPC. Comparisons are made between the control performances of MPC and PI strategies. Simulation results show that although both MPC and PI schemes can stabilize the DWC in case of feed disturbances, MPC generally behaves better than the PI strategy for both composition control and tempera- ture inferential control, resulting in a more stable and superior performance with lower values of integral of squared error （ISE）.
基金Supported by the National Natural Science Foundation of China(21676299,21476261,21506255)
文摘In this paper, the mixture of dimethyl carbonate, ethyl methyl carbonate and diethyl carbonate was separated by middle-vessel batch distillation with feeding in middle-vessel and process control characteristics were researched. The steady state simulation results in Aspen Plus were exported to Aspen Dynamics. Then control effect of liquid level control with HighSelector, composition control(structure1, structure2) and temperature control(proportional action, proportional integration action) were proposed. Composition control structure 2 and temperature control with PI action were investigated to achieve a good control effect.
基金the National Key Basic Research Program of China (Grant: 2013CB834204)the National Natural Science Foundation of China (Grant: 61300242, 61772291)+1 种基金the Tianjin Research Program of Application Foundation and Advanced Technology (Grant: 15JCQNJC41500, 17JCZDJC30500)the Open Project Foundation of Information Security Evaluation Center of Civil Aviation, Civil Aviation University of China (Grant: CAAC-ISECCA- 201701, CAAC-ISECCA-201702)
文摘Nowadays, machine learning is widely used in malware detection system as a core component. The machine learning algorithm is designed under the assumption that all datasets follow the same underlying data distribution. But the real-world malware data distribution is not stable and changes with time. By exploiting the knowledge of the machine learning algorithm and malware data concept drift problem, we show a novel learning evasive botnet architecture and a stealthy and secure C&C mechanism. Based on the email communication channel, we construct a stealthy email-based P2 P-like botnet that exploit the excellent reputation of email servers and a huge amount of benign email communication in the same channel. The experiment results show horizontal correlation learning algorithm is difficult to separate malicious email traffic from normal email traffic based on the volume features and time-related features with enough confidence. We discuss the malware data concept drift and possible defense strategies.
基金partially supported by the National Science Fund for Distinguished Young Scholars(62025205)the National Natural Science Foundation of China(Grant No.62032020).
文摘Persuasion,as one of the crucial abilities in human communication,has garnered extensive attention from researchers within the field of intelligent dialogue systems.Developing dialogue agents that can persuade others to accept certain standpoints is essential to achieving truly intelligent and anthropomorphic dialogue systems.Benefiting from the substantial progress of Large Language Models(LLMs),dialogue agents have acquired an exceptional capability in context understanding and response generation.However,as a typical and complicated cognitive psychological system,persuasive dialogue agents also require knowledge from the domain of cognitive psychology to attain a level of human-like persuasion.Consequently,the cognitive strategy-enhanced persuasive dialogue agent(defined as CogAgent),which incorporates cognitive strategies to achieve persuasive targets through conversation,has become a predominant research paradigm.To depict the research trends of CogAgent,in this paper,we first present several fundamental cognitive psychology theories and give the formalized definition of three typical cognitive strategies,including the persuasion strategy,the topic path planning strategy,and the argument structure prediction strategy.Then we propose a new system architecture by incorporating the formalized definition to lay the foundation of CogAgent.Representative works are detailed and investigated according to the combined cognitive strategy,followed by the summary of authoritative benchmarks and evaluation metrics.Finally,we summarize our insights on open issues and future directions of CogAgent for upcoming researchers.
基金supported by the National Science Fund for Distinguished Young Scholars(62025205)the National Natural Science Foundation of China(Grant No.62032020)the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University.
文摘Video-Grounded Dialogue System(VGDS),focusing on generating reasonable responses based on multiturn dialogue contexts and a given video,has received intensive attention recently.The key to building a superior VGDS lies in efficiently reasoning over visual and textual concepts of various granularities and achieving comprehensive visual-textual multimodality alignment.Despite remarkable research progress,existing studies suffer from identifying context-relevant video parts while disregarding the impact of redundant information in long-form and content-dynamic videos.Further,current methods usually align all semantics in different modalities uniformly using a one-time cross-attention scheme,which neglects the sophisticated correspondence between various granularities of visual and textual concepts(e.g.,still objects with nouns,dynamic events with verbs).To this end,we propose a novel system,namely Cascade cOntext-oriented Spatio-Temporal Attention Network(COSTA),to generate reasonable responses efficiently and accurately.Specifically,COSTA first adopts a cascade attention network to localize only the most relevant video clips and regions in a coarse-tofine manner which effectively filters the irrelevant visual semantics.Secondly,we design a memory distillation-inspired iterative visual-textual cross-attention strategy to progressively integrate visual semantics with dialogue contexts across varying granularities,facilitating extensive multi-modal alignment.Experiments on several benchmarks demonstrate significant improvements in our model over state-of-the-art methods across various metrics.
基金supported by the National Natural Science Foundation of China[grant number 82100576]Guangdong Basic and Applied Basic Research Foundation[grant number 2020A1515111087]China Postdoctoral Science Foundation[grant number 2021M703750].
文摘Acute severe lower gastrointestinal bleeding is a rare but potentially fatal complication of Crohn's disease(CD),affecting between 0.6%and 5.5%of CD patients during their lifelong disease course.Managing bleeding episodes effectively hinges on vital resuscitation.Endoscopic evaluation and computed tomography play crucial roles in accurate identification and intervention.Fortunately,most bleeding episodes can be successfully managed through appropriate conservative treatment.Medical therapies,particularly infliximab,aim to induce and maintain mucosal healing and serve as the leading treatment approach.Minimally invasive procedures,such as endoscopic hemostasis and angio-embolization,can achieve immediate hemostasis.Surgical treatment is only considered a last resort when conservative therapies fail.Despite achieving hemostasis,the risk of rebleeding ranges from 19.0%to 50.5%.The objective of this review is to provide a comprehensive and updated overview of the clinical manifestations,diagnostic methods,therapeutic approaches,and prognostic outcomes associated with acute severe gastrointestinal bleeding in CD.Furthermore,we aimed to propose a management algorithm to assist clinicians in the effective management of this condition.
