Located on the western of Sichuan, the east border of Tibet plateau, Xianshuihe fault is a significant strong earthquake zone. From Huiyuansi pull\|apart basin in Qianning, Xianshuihe fault can be divided two segments...Located on the western of Sichuan, the east border of Tibet plateau, Xianshuihe fault is a significant strong earthquake zone. From Huiyuansi pull\|apart basin in Qianning, Xianshuihe fault can be divided two segments\|NW section and SE section: the construction of the former is single and a main fault; the construction of the latter is complex and composed by three parallel faults, its main fault is named as Selaha—Kangding fault, which distributes along Jinlongsi, Sehala, Mugecuo and Kangding. Yalahe fault, located at the NE direction of the main fault, and Zeduotang fault, located at the SW direction of the main fault, are all secondary faults, which are 9～13km away from the main fault. At the south of Kangding, the segment of Xianshuihe fault is a single main section, called as Moxi fault. On the basis of recent researching results, this paper mainly discusses the slip rate and recurrence interval of strong earthquake of the SE segment (Qianning—Kangding) on Xianshuihe.展开更多
In this paper, the general calculation formulas of radial matrix elements for relativistic n-dimensional hydrogen atom of spin S=0 are obtained, and the recurrence relation of different power order radial matrix eleme...In this paper, the general calculation formulas of radial matrix elements for relativistic n-dimensional hydrogen atom of spin S=0 are obtained, and the recurrence relation of different power order radial matrix elements are also derived.展开更多
Recognition of pulse repetition interval(PRI)modulation is a fundamental task in the interpretation of radar intentions.However,the existing PRI modulation recognition methods mainly focus on single-label classificati...Recognition of pulse repetition interval(PRI)modulation is a fundamental task in the interpretation of radar intentions.However,the existing PRI modulation recognition methods mainly focus on single-label classification of PRI sequences.The prerequisite for the effectiveness of these methods is that the PRI sequences are perfectly divided according to different modulation types before identification,while the actual situation is that radar pulses reach the receiver continuously,and there is no completely reliable method to achieve this division in the case of non-cooperative reception.Based on the above actual needs,this paper implements an algorithm based on the recurrence plot technique and the multi-target detection model,which does not need to divide the PRI sequence in advance.Compared with the sliding window method,it can more effectively realize the recognition of the dynamically varying PRI mo dulation.展开更多
High complexity and uncertainty of air combat pose significant challenges to target intention prediction.Current interpolation methods for data pre-processing and wrangling have limitations in capturing interrelations...High complexity and uncertainty of air combat pose significant challenges to target intention prediction.Current interpolation methods for data pre-processing and wrangling have limitations in capturing interrelationships among intricate variable patterns.Accordingly,this study proposes a Mogrifier gate recurrent unit-D(Mog-GRU-D)model to address the com-bat target intention prediction issue under the incomplete infor-mation condition.The proposed model directly processes miss-ing data while reducing the independence between inputs and output states.A total of 1200 samples from twelve continuous moments are captured through the combat simulation system,each of which consists of seven dimensional features.To bench-mark the experiment,a missing valued dataset has been gener-ated by randomly removing 20%of the original data.Extensive experiments demonstrate that the proposed model obtains the state-of-the-art performance with an accuracy of 73.25%when dealing with incomplete information.This study provides possi-ble interpretations for the principle of target interactive mecha-nism,highlighting the model’s effectiveness in potential air war-fare implementation.展开更多
[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-base...[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.展开更多
Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties ...Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties in dealing with high dimensional time series target data, a threat assessment method based on self-attention mechanism and gated recurrent unit(SAGRU) is proposed. Firstly, a threat feature system including air combat situations and capability features is established. Moreover, a data augmentation process based on fractional Fourier transform(FRFT) is applied to extract more valuable information from time series situation features. Furthermore, aiming to capture key characteristics of battlefield evolution, a bidirectional GRU and SA mechanisms are designed for enhanced features.Subsequently, after the concatenation of the processed air combat situation and capability features, the target threat level will be predicted by fully connected neural layers and the softmax classifier. Finally, in order to validate this model, an air combat dataset generated by a combat simulation system is introduced for model training and testing. The comparison experiments show the proposed model has structural rationality and can perform threat assessment faster and more accurately than the other existing models based on deep learning.展开更多
This work proposes a recorded recurrent twin delayed deep deterministic(RRTD3)policy gradient algorithm to solve the challenge of constructing guidance laws for intercepting endoatmospheric maneuvering missiles with u...This work proposes a recorded recurrent twin delayed deep deterministic(RRTD3)policy gradient algorithm to solve the challenge of constructing guidance laws for intercepting endoatmospheric maneuvering missiles with uncertainties and observation noise.The attack-defense engagement scenario is modeled as a partially observable Markov decision process(POMDP).Given the benefits of recurrent neural networks(RNNs)in processing sequence information,an RNN layer is incorporated into the agent’s policy network to alleviate the bottleneck of traditional deep reinforcement learning methods while dealing with POMDPs.The measurements from the interceptor’s seeker during each guidance cycle are combined into one sequence as the input to the policy network since the detection frequency of an interceptor is usually higher than its guidance frequency.During training,the hidden states of the RNN layer in the policy network are recorded to overcome the partially observable problem that this RNN layer causes inside the agent.The training curves show that the proposed RRTD3 successfully enhances data efficiency,training speed,and training stability.The test results confirm the advantages of the RRTD3-based guidance laws over some conventional guidance laws.展开更多
Objective:Pelvic organ prolapse(POP)is a common condition in postmenopausal women,with an increasing prevalence due to aging.Some women experience POP recurrence after surgical treatment,significantly affecting their ...Objective:Pelvic organ prolapse(POP)is a common condition in postmenopausal women,with an increasing prevalence due to aging.Some women experience POP recurrence after surgical treatment,significantly affecting their physical and mental health.The uterosacral ligament is a critical pelvic support structure.This study aims to investigate the molecular pathological changes in the uterosacral ligament of postmenopausal women with recurrent POP using transcriptomic analysis.Methods:Transcriptomic data of uterosacral ligament tissues were obtained from the public dataset GSE28660,which includes samples from 4 postmenopausal women with recurrent POP,4 with primary POP,and 4 without POP.Differentially expressed genes(DEGs)were identified between recurrent POP and both primary and non-POP groups.Further analysis included intersection analysis of DEGs,gene ontology enrichment,protein protein interaction(PPI)network construction,gene set enrichment analysis(GSEA),single-sample GSEA,and xCell immune cell infiltration analysis to explore molecular pathological changes in recurrent POP.Additionally,histological and molecular differences in the uterosacral ligament were compared between simulated vaginal delivery(SVD)rat models with and without ovariectomy.Results:Compared with primary POP and non-POP groups,recurrent POP exhibited activation of adipogenesis and inflammation-related pathways,while pathways related to muscle proliferation and contraction were downregulated in the uterosacral ligament.Nine key DEGs(ADIPOQ,FABP4,IL-6,LIPE,LPL,PCK1,PLIN1,PPARG,and CD36)were identified,with most enriched in the peroxisome proliferator-activated receptor(PPAR)signaling pathway.These genes were significantly correlated with lipid accumulation,monocyte infiltration,and neutrophil infiltration in the uterosacral ligament.Urodynamic testing revealed that the bladder leak point pressure was significantly higher in ovariectomized SVD rats,both of which had higher values than the sham group.Masson staining showed pronounced adipogenesis in the uterosacral ligament of ovariectomized SVD rats,along with reduced collagen and muscle fibers compared to the sham and non ovariectomized SVD groups.Furthermore,real-time RT-PCR confirmed significantly elevated expression of key DEGs,including ADIPOQ,IL-6,PCK1,and PLIN1,in the uterosacral ligaments of ovariectomized SVD rats.Conclusion:Adipogenesis and inflammation in the uterosacral ligament may contribute to its reduced supportive function,potentially leading to recurrence POP in postmenopausal women.展开更多
The pre-warning of abnormal energy consumption is important for energy conservation of industrial engineering. However, related studies on the lead smelting industries which usually have a huge energy consumption are ...The pre-warning of abnormal energy consumption is important for energy conservation of industrial engineering. However, related studies on the lead smelting industries which usually have a huge energy consumption are rarely reported. Therefore, a pre-warning system was established in this study based on the intelligent prediction of energy consumption and the identification of abnormal energy consumption. A least square support vector regression (LSSVR) model optimized by the adaptive genetic algorithm was developed to predict the energy consumption in the process of lead smelting. A recurrence plots (RP) analysis and a confidence intervals (CI) analysis were conducted to quantitatively confirm the stationary degree of energy consumption and the normal range of energy consumption, respectively, to realize the identification of abnormal energy consumption. It is found the prediction accuracy of LSSVR model can exceed 90% based on the comparison between the actual and predicted data. The energy consumption is considered to be non-stationary if the correlation coefficient between the time series of periodicity and energy consumption is larger than that between the time series of periodicity and Lorenz. Additionally, the lower limit and upper limit of normal energy consumption are obtained.展开更多
According to the two-dimensional(2-D) thermo-elasticity theory, the exact elasticity solution of the simply supported laminated beams subjected to thermo-loads was studied. An analytical method was presented to obtain...According to the two-dimensional(2-D) thermo-elasticity theory, the exact elasticity solution of the simply supported laminated beams subjected to thermo-loads was studied. An analytical method was presented to obtain the temperature, displacement and stress fields in the beam. Firstly, the general solutions of temperature, displacements and stresses for a single-layered simply supported beam were obtained by solving the 2-D heat conduction equation and the 2-D elasticity equations, respectively. Then, based on the continuity of temperature, heat flux, displacements and stresses on the interface of two adjacent layers, the formulae of temperature, displacements and stresses between the lowest layer and the top layer of the beam were derived out in a recurrent manner. Finally, the unknown coefficients in the solutions were determined by the use of the upper surface and lower surface conditions of the beam. The distributions of temperature, displacement and stress in the beam were obtained by substituting these coefficients back to the recurrence formulae and the solutions. The excellent convergence of the present method has been demonstrated and the results obtained by the present method agree well with those from the finite element method. The effects of surface temperatures, thickness, layer number and material properties of the plate on the temperature distribution were discussed in detail. Numerical results reveal that the displacements and stresses monotonically increase with the increase of surface temperatures. In particular, the horizontal stresses are discontinuous at the interface.展开更多
Tumor cells escape host immune surveillance bydown-regulation of MHC and/or co-stimulatorymolecules.Anti-tumor immune responses are mediated primarily by T cells.A deficiency in either MHC or co-stimulatory molecules ...Tumor cells escape host immune surveillance bydown-regulation of MHC and/or co-stimulatorymolecules.Anti-tumor immune responses are mediated primarily by T cells.A deficiency in either MHC or co-stimulatory molecules on tumor cells is associated with a failure to induce anti-tumor immunity.展开更多
Over the past 10 years adoptive immunotherapieshave been developed for cancer treatment. Cytotoxic Tlymphocytes (CTL) play a major role in host antitumorimmune response. The perforin and Fas ligand (Fas-L)pathways whi...Over the past 10 years adoptive immunotherapieshave been developed for cancer treatment. Cytotoxic Tlymphocytes (CTL) play a major role in host antitumorimmune response. The perforin and Fas ligand (Fas-L)pathways which were two major mechanisms are res-ponsible for tumor cell death by CTLs. A major obstacleto the application of adoptive imunotherapy in thetreatment of human malignancy has been the inability展开更多
In order to improve the autonomous ability of unmanned aerial vehicles(UAV)to implement air combat mission,many artificial intelligence-based autonomous air combat maneuver decision-making studies have been carried ou...In order to improve the autonomous ability of unmanned aerial vehicles(UAV)to implement air combat mission,many artificial intelligence-based autonomous air combat maneuver decision-making studies have been carried out,but these studies are often aimed at individual decision-making in 1 v1 scenarios which rarely happen in actual air combat.Based on the research of the 1 v1 autonomous air combat maneuver decision,this paper builds a multi-UAV cooperative air combat maneuver decision model based on multi-agent reinforcement learning.Firstly,a bidirectional recurrent neural network(BRNN)is used to achieve communication between UAV individuals,and the multi-UAV cooperative air combat maneuver decision model under the actor-critic architecture is established.Secondly,through combining with target allocation and air combat situation assessment,the tactical goal of the formation is merged with the reinforcement learning goal of every UAV,and a cooperative tactical maneuver policy is generated.The simulation results prove that the multi-UAV cooperative air combat maneuver decision model established in this paper can obtain the cooperative maneuver policy through reinforcement learning,the cooperative maneuver policy can guide UAVs to obtain the overall situational advantage and defeat the opponents under tactical cooperation.展开更多
文摘Located on the western of Sichuan, the east border of Tibet plateau, Xianshuihe fault is a significant strong earthquake zone. From Huiyuansi pull\|apart basin in Qianning, Xianshuihe fault can be divided two segments\|NW section and SE section: the construction of the former is single and a main fault; the construction of the latter is complex and composed by three parallel faults, its main fault is named as Selaha—Kangding fault, which distributes along Jinlongsi, Sehala, Mugecuo and Kangding. Yalahe fault, located at the NE direction of the main fault, and Zeduotang fault, located at the SW direction of the main fault, are all secondary faults, which are 9～13km away from the main fault. At the south of Kangding, the segment of Xianshuihe fault is a single main section, called as Moxi fault. On the basis of recent researching results, this paper mainly discusses the slip rate and recurrence interval of strong earthquake of the SE segment (Qianning—Kangding) on Xianshuihe.
文摘In this paper, the general calculation formulas of radial matrix elements for relativistic n-dimensional hydrogen atom of spin S=0 are obtained, and the recurrence relation of different power order radial matrix elements are also derived.
基金supported by the National Defense Science and Technology Outstanding Youth Science Fund Project(2018-JCJQ-ZQ-023)the Hunan Provincial Natural Science Foundation of Innovation Research Group Project(2019JJ10004)。
文摘Recognition of pulse repetition interval(PRI)modulation is a fundamental task in the interpretation of radar intentions.However,the existing PRI modulation recognition methods mainly focus on single-label classification of PRI sequences.The prerequisite for the effectiveness of these methods is that the PRI sequences are perfectly divided according to different modulation types before identification,while the actual situation is that radar pulses reach the receiver continuously,and there is no completely reliable method to achieve this division in the case of non-cooperative reception.Based on the above actual needs,this paper implements an algorithm based on the recurrence plot technique and the multi-target detection model,which does not need to divide the PRI sequence in advance.Compared with the sliding window method,it can more effectively realize the recognition of the dynamically varying PRI mo dulation.
基金supported by the Aeronautical Science Foundation of China(2020Z023053002).
文摘High complexity and uncertainty of air combat pose significant challenges to target intention prediction.Current interpolation methods for data pre-processing and wrangling have limitations in capturing interrelationships among intricate variable patterns.Accordingly,this study proposes a Mogrifier gate recurrent unit-D(Mog-GRU-D)model to address the com-bat target intention prediction issue under the incomplete infor-mation condition.The proposed model directly processes miss-ing data while reducing the independence between inputs and output states.A total of 1200 samples from twelve continuous moments are captured through the combat simulation system,each of which consists of seven dimensional features.To bench-mark the experiment,a missing valued dataset has been gener-ated by randomly removing 20%of the original data.Extensive experiments demonstrate that the proposed model obtains the state-of-the-art performance with an accuracy of 73.25%when dealing with incomplete information.This study provides possi-ble interpretations for the principle of target interactive mecha-nism,highlighting the model’s effectiveness in potential air war-fare implementation.
文摘[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.
基金supported by the National Natural Science Foundation of China (6202201562088101)+1 种基金Shanghai Municipal Science and Technology Major Project (2021SHZDZX0100)Shanghai Municip al Commission of Science and Technology Project (19511132101)。
文摘Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties in dealing with high dimensional time series target data, a threat assessment method based on self-attention mechanism and gated recurrent unit(SAGRU) is proposed. Firstly, a threat feature system including air combat situations and capability features is established. Moreover, a data augmentation process based on fractional Fourier transform(FRFT) is applied to extract more valuable information from time series situation features. Furthermore, aiming to capture key characteristics of battlefield evolution, a bidirectional GRU and SA mechanisms are designed for enhanced features.Subsequently, after the concatenation of the processed air combat situation and capability features, the target threat level will be predicted by fully connected neural layers and the softmax classifier. Finally, in order to validate this model, an air combat dataset generated by a combat simulation system is introduced for model training and testing. The comparison experiments show the proposed model has structural rationality and can perform threat assessment faster and more accurately than the other existing models based on deep learning.
基金supported by the National Natural Science Foundation of China(Grant No.12072090)。
文摘This work proposes a recorded recurrent twin delayed deep deterministic(RRTD3)policy gradient algorithm to solve the challenge of constructing guidance laws for intercepting endoatmospheric maneuvering missiles with uncertainties and observation noise.The attack-defense engagement scenario is modeled as a partially observable Markov decision process(POMDP).Given the benefits of recurrent neural networks(RNNs)in processing sequence information,an RNN layer is incorporated into the agent’s policy network to alleviate the bottleneck of traditional deep reinforcement learning methods while dealing with POMDPs.The measurements from the interceptor’s seeker during each guidance cycle are combined into one sequence as the input to the policy network since the detection frequency of an interceptor is usually higher than its guidance frequency.During training,the hidden states of the RNN layer in the policy network are recorded to overcome the partially observable problem that this RNN layer causes inside the agent.The training curves show that the proposed RRTD3 successfully enhances data efficiency,training speed,and training stability.The test results confirm the advantages of the RRTD3-based guidance laws over some conventional guidance laws.
基金supported by the Key Research and Development Program of Hunan Province(2023SK2038)the Natural Science Foundation of Hunan Province(2024JJ8121),China。
文摘Objective:Pelvic organ prolapse(POP)is a common condition in postmenopausal women,with an increasing prevalence due to aging.Some women experience POP recurrence after surgical treatment,significantly affecting their physical and mental health.The uterosacral ligament is a critical pelvic support structure.This study aims to investigate the molecular pathological changes in the uterosacral ligament of postmenopausal women with recurrent POP using transcriptomic analysis.Methods:Transcriptomic data of uterosacral ligament tissues were obtained from the public dataset GSE28660,which includes samples from 4 postmenopausal women with recurrent POP,4 with primary POP,and 4 without POP.Differentially expressed genes(DEGs)were identified between recurrent POP and both primary and non-POP groups.Further analysis included intersection analysis of DEGs,gene ontology enrichment,protein protein interaction(PPI)network construction,gene set enrichment analysis(GSEA),single-sample GSEA,and xCell immune cell infiltration analysis to explore molecular pathological changes in recurrent POP.Additionally,histological and molecular differences in the uterosacral ligament were compared between simulated vaginal delivery(SVD)rat models with and without ovariectomy.Results:Compared with primary POP and non-POP groups,recurrent POP exhibited activation of adipogenesis and inflammation-related pathways,while pathways related to muscle proliferation and contraction were downregulated in the uterosacral ligament.Nine key DEGs(ADIPOQ,FABP4,IL-6,LIPE,LPL,PCK1,PLIN1,PPARG,and CD36)were identified,with most enriched in the peroxisome proliferator-activated receptor(PPAR)signaling pathway.These genes were significantly correlated with lipid accumulation,monocyte infiltration,and neutrophil infiltration in the uterosacral ligament.Urodynamic testing revealed that the bladder leak point pressure was significantly higher in ovariectomized SVD rats,both of which had higher values than the sham group.Masson staining showed pronounced adipogenesis in the uterosacral ligament of ovariectomized SVD rats,along with reduced collagen and muscle fibers compared to the sham and non ovariectomized SVD groups.Furthermore,real-time RT-PCR confirmed significantly elevated expression of key DEGs,including ADIPOQ,IL-6,PCK1,and PLIN1,in the uterosacral ligaments of ovariectomized SVD rats.Conclusion:Adipogenesis and inflammation in the uterosacral ligament may contribute to its reduced supportive function,potentially leading to recurrence POP in postmenopausal women.
基金Project(2015SK1002) supported by Key Projects of Hunan Province Science and Technology Plan,China
文摘The pre-warning of abnormal energy consumption is important for energy conservation of industrial engineering. However, related studies on the lead smelting industries which usually have a huge energy consumption are rarely reported. Therefore, a pre-warning system was established in this study based on the intelligent prediction of energy consumption and the identification of abnormal energy consumption. A least square support vector regression (LSSVR) model optimized by the adaptive genetic algorithm was developed to predict the energy consumption in the process of lead smelting. A recurrence plots (RP) analysis and a confidence intervals (CI) analysis were conducted to quantitatively confirm the stationary degree of energy consumption and the normal range of energy consumption, respectively, to realize the identification of abnormal energy consumption. It is found the prediction accuracy of LSSVR model can exceed 90% based on the comparison between the actual and predicted data. The energy consumption is considered to be non-stationary if the correlation coefficient between the time series of periodicity and energy consumption is larger than that between the time series of periodicity and Lorenz. Additionally, the lower limit and upper limit of normal energy consumption are obtained.
基金Project(2012CB026205)supported by the National Basic Research Program of ChinaProject(51238003)supported by the National Natural Science Foundation of ChinaProject(2014Y01)supported by the Transportation Department of Jiangsu Province,China
文摘According to the two-dimensional(2-D) thermo-elasticity theory, the exact elasticity solution of the simply supported laminated beams subjected to thermo-loads was studied. An analytical method was presented to obtain the temperature, displacement and stress fields in the beam. Firstly, the general solutions of temperature, displacements and stresses for a single-layered simply supported beam were obtained by solving the 2-D heat conduction equation and the 2-D elasticity equations, respectively. Then, based on the continuity of temperature, heat flux, displacements and stresses on the interface of two adjacent layers, the formulae of temperature, displacements and stresses between the lowest layer and the top layer of the beam were derived out in a recurrent manner. Finally, the unknown coefficients in the solutions were determined by the use of the upper surface and lower surface conditions of the beam. The distributions of temperature, displacement and stress in the beam were obtained by substituting these coefficients back to the recurrence formulae and the solutions. The excellent convergence of the present method has been demonstrated and the results obtained by the present method agree well with those from the finite element method. The effects of surface temperatures, thickness, layer number and material properties of the plate on the temperature distribution were discussed in detail. Numerical results reveal that the displacements and stresses monotonically increase with the increase of surface temperatures. In particular, the horizontal stresses are discontinuous at the interface.
文摘Tumor cells escape host immune surveillance bydown-regulation of MHC and/or co-stimulatorymolecules.Anti-tumor immune responses are mediated primarily by T cells.A deficiency in either MHC or co-stimulatory molecules on tumor cells is associated with a failure to induce anti-tumor immunity.
文摘Over the past 10 years adoptive immunotherapieshave been developed for cancer treatment. Cytotoxic Tlymphocytes (CTL) play a major role in host antitumorimmune response. The perforin and Fas ligand (Fas-L)pathways which were two major mechanisms are res-ponsible for tumor cell death by CTLs. A major obstacleto the application of adoptive imunotherapy in thetreatment of human malignancy has been the inability
基金supported by the Aeronautical Science Foundation of China(2017ZC53033)the Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University(CX2020156)。
文摘In order to improve the autonomous ability of unmanned aerial vehicles(UAV)to implement air combat mission,many artificial intelligence-based autonomous air combat maneuver decision-making studies have been carried out,but these studies are often aimed at individual decision-making in 1 v1 scenarios which rarely happen in actual air combat.Based on the research of the 1 v1 autonomous air combat maneuver decision,this paper builds a multi-UAV cooperative air combat maneuver decision model based on multi-agent reinforcement learning.Firstly,a bidirectional recurrent neural network(BRNN)is used to achieve communication between UAV individuals,and the multi-UAV cooperative air combat maneuver decision model under the actor-critic architecture is established.Secondly,through combining with target allocation and air combat situation assessment,the tactical goal of the formation is merged with the reinforcement learning goal of every UAV,and a cooperative tactical maneuver policy is generated.The simulation results prove that the multi-UAV cooperative air combat maneuver decision model established in this paper can obtain the cooperative maneuver policy through reinforcement learning,the cooperative maneuver policy can guide UAVs to obtain the overall situational advantage and defeat the opponents under tactical cooperation.
