A safe and reliable path planning algorithm is fundamental for unmanned surface vehicles(USVs)to perform autonomous navigation tasks.However,a single global or local planning strategy cannot fully meet the requirement...A safe and reliable path planning algorithm is fundamental for unmanned surface vehicles(USVs)to perform autonomous navigation tasks.However,a single global or local planning strategy cannot fully meet the requirements of complex maritime environments.Global planning alone cannot effectively handle dynamic obstacles,while local planning alone may fall into local optima.To address these issues,this paper proposes a multi-dynamic-obstacle avoidance path planning method that integrates an improved A^(*)algorithm with the dynamic window approach(DWA).The traditional A^(*)algorithm often generates paths that are too close to obstacle boundaries and contain excessive turning points,whereas the traditional DWA tends to skirt densely clustered obstacles,resulting in longer routes and insufficient dynamic obstacle avoidance.To overcome these limitations,improved versions of both algorithms are developed.Key points extracted from the optimized A^(*)path are used as intermediate start-destination pairs for the improved DWA,and the weights of the DWA evaluation function are adjusted to achieve effective fusion.Furthermore,a multi-dynamic-obstacle avoidance strategy is designed for complex navigation scenarios.Simulation results demonstrate that the USV can adaptively switch between dynamic obstacle avoidance and path tracking based on obstacle distribution,validating the effectiveness of the proposed method.展开更多
Acute lung injury(ALI)is a severe inflammatory condition with a high mortality rate,often precipitated by sepsis.The pathophysiology of ALI involves complex mechanisms,including inflammation,oxidative stress,and ferro...Acute lung injury(ALI)is a severe inflammatory condition with a high mortality rate,often precipitated by sepsis.The pathophysiology of ALI involves complex mechanisms,including inflammation,oxidative stress,and ferroptosis,a novel form of regulated cell death.This study explores the therapeutic potential of andrographolide(AG),a bioactive compound derived from Andrographis,in mitigating Lipopolysaccharide(LPS)-induced inflammation and ferroptosis.Our research employed in vitro experiments with RAW264.7 macrophage cells and in vivo studies using a murine model of LPS-induced ALI.The results indicate that AG significantly suppresses the production of pro-inflammatory cytokines and inhibits ferroptosis in LPS-stimulated RAW264.7 cells.In vivo,AG treatment markedly reduces lung edema,decreases inflammatory cell infiltration,and mitigates ferroptosis in lung tissues of LPS-induced ALI mice.These protective effects are mediated via the modulation of the Toll-like receptor 4(TLR4)/Kelch-like ECH-associated protein 1(Keap1)/Nuclear factor erythroid 2-related factor 2(Nrf2)signaling pathway.Molecular docking simulations identified the binding sites of AG on the TLR4 protein(Kd value:-33.5 kcal·mol^(-1)),and these interactions were further corroborated by Cellular Thermal Shift Assay(CETSA)and SPR assays.Collectively,our findings demonstrate that AG exerts potent anti-inflammatory and anti-ferroptosis effects in LPS-induced ALI by targeting TLR4 and modulating the Keap1/Nrf2 pathway.This study underscores AG's potential as a therapeutic agent for ALI and provides new insights into its underlying mechanisms of action.展开更多
At present, universal generating function(UGF) is a reliability evaluation technique which holds the bare-looking and easily program-realized merits in multi-state system. Thus, it is meaningful to apply this method t...At present, universal generating function(UGF) is a reliability evaluation technique which holds the bare-looking and easily program-realized merits in multi-state system. Thus, it is meaningful to apply this method to an actual industry system. Compressor systems in natural gas pipelines are series-parallel multi-state systems,where the compressor units in each compressor station work in a parallel way and these pressure-boosting stations in the pipeline are series connected. Considering the characteristic of gas pipelines, this paper develops two different UGFs to evaluate the system reliability. One(Model 1) establishes a system model from every compressor unit while the other(Model 2) considers the whole system as a combination of multi-state components. Besides, all the parameters of "weight" in UGFs are obtained from thermal-hydraulic models based on the actual engineering and"probability" from Monte Carlo simulation. The results show that the system reliabilities calculated by different UGFs are approximately equal. In addition, the demand of gas and the gas pipeline transportation system show a reverse trend. Because the number of parameters needed in Model 2 is far less than that needed in Model 1,Model 2 is simpler programming and faster solved.展开更多
目的综合考虑B型超声(B-mode ultrasound,B-US)和对比增强超声(contrast-enhanced ultrasound,CEUS)双模态信息有助于提升乳腺肿瘤诊断的准确性,从而利于提高患者生存率。然而,目前大多数模型只关注B-US的特征提取,忽视了CEUS特征的学...目的综合考虑B型超声(B-mode ultrasound,B-US)和对比增强超声(contrast-enhanced ultrasound,CEUS)双模态信息有助于提升乳腺肿瘤诊断的准确性,从而利于提高患者生存率。然而,目前大多数模型只关注B-US的特征提取,忽视了CEUS特征的学习和双模态信息的融合处理。为解决上述问题,提出了一个融合时空特征与时间约束的双模态乳腺肿瘤诊断模型(spatio-temporal feature and temporal-constrained model,STFTCM)。方法首先,基于双模态信息的数据特点,采用异构双分支网络学习B-US和CEUS包含的时空特征。然后,设计时间注意力损失函数引导CEUS分支关注造影剂流入病灶区的时间窗口,从该窗口期内提取CEUS特征。最后,借助特征融合模块实现双分支网络之间的横向连接,通过将B-US特征作为CEUS分支补充信息的方式,完成双模态特征融合。结果在收集到的数据集上进行对比实验,STFTCM预测的正确率、敏感性、宏平均F1和AUC(area under the curve)指标均表现优秀,其中预测正确率达88.2%,领先于其他先进模型。消融实验中,时间注意力约束将模型预测正确率提升5.8%,特征融合使得模型诊断正确率相较于单分支模型至少提升2.9%。结论本文提出的STFTCM能有效地提取并融合处理B-US和CEUS双模态信息,给出准确的诊断结果。同时,时间注意力约束和特征融合模块可以显著地提升模型性能。展开更多
基金supported by the National Nature Science Foundation of China(62203299,62373246,62388101)the Research Fund of State Key Laboratory of Deep-Sea Manned Vehicles(2024SKLDMV04)+1 种基金the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University(SL2023MS007)the Startup Fund for Young Faculty at SJTU(24X010502929)。
文摘A safe and reliable path planning algorithm is fundamental for unmanned surface vehicles(USVs)to perform autonomous navigation tasks.However,a single global or local planning strategy cannot fully meet the requirements of complex maritime environments.Global planning alone cannot effectively handle dynamic obstacles,while local planning alone may fall into local optima.To address these issues,this paper proposes a multi-dynamic-obstacle avoidance path planning method that integrates an improved A^(*)algorithm with the dynamic window approach(DWA).The traditional A^(*)algorithm often generates paths that are too close to obstacle boundaries and contain excessive turning points,whereas the traditional DWA tends to skirt densely clustered obstacles,resulting in longer routes and insufficient dynamic obstacle avoidance.To overcome these limitations,improved versions of both algorithms are developed.Key points extracted from the optimized A^(*)path are used as intermediate start-destination pairs for the improved DWA,and the weights of the DWA evaluation function are adjusted to achieve effective fusion.Furthermore,a multi-dynamic-obstacle avoidance strategy is designed for complex navigation scenarios.Simulation results demonstrate that the USV can adaptively switch between dynamic obstacle avoidance and path tracking based on obstacle distribution,validating the effectiveness of the proposed method.
基金supported by China-ASEAN International Innovative Center for Health Industry of Traditional Chinese Medicine(No.AD20297142)Guangxi Collaborative Innovation Center for Scientific Achievements Transformation and Applicationon Traditional Chinese Medicine(No.05020058)。
文摘Acute lung injury(ALI)is a severe inflammatory condition with a high mortality rate,often precipitated by sepsis.The pathophysiology of ALI involves complex mechanisms,including inflammation,oxidative stress,and ferroptosis,a novel form of regulated cell death.This study explores the therapeutic potential of andrographolide(AG),a bioactive compound derived from Andrographis,in mitigating Lipopolysaccharide(LPS)-induced inflammation and ferroptosis.Our research employed in vitro experiments with RAW264.7 macrophage cells and in vivo studies using a murine model of LPS-induced ALI.The results indicate that AG significantly suppresses the production of pro-inflammatory cytokines and inhibits ferroptosis in LPS-stimulated RAW264.7 cells.In vivo,AG treatment markedly reduces lung edema,decreases inflammatory cell infiltration,and mitigates ferroptosis in lung tissues of LPS-induced ALI mice.These protective effects are mediated via the modulation of the Toll-like receptor 4(TLR4)/Kelch-like ECH-associated protein 1(Keap1)/Nuclear factor erythroid 2-related factor 2(Nrf2)signaling pathway.Molecular docking simulations identified the binding sites of AG on the TLR4 protein(Kd value:-33.5 kcal·mol^(-1)),and these interactions were further corroborated by Cellular Thermal Shift Assay(CETSA)and SPR assays.Collectively,our findings demonstrate that AG exerts potent anti-inflammatory and anti-ferroptosis effects in LPS-induced ALI by targeting TLR4 and modulating the Keap1/Nrf2 pathway.This study underscores AG's potential as a therapeutic agent for ALI and provides new insights into its underlying mechanisms of action.
基金the National Natural Science Foundation of China(No.51504271)the National Science & Technology Specific Project(No.2016ZX05066005-001)
文摘At present, universal generating function(UGF) is a reliability evaluation technique which holds the bare-looking and easily program-realized merits in multi-state system. Thus, it is meaningful to apply this method to an actual industry system. Compressor systems in natural gas pipelines are series-parallel multi-state systems,where the compressor units in each compressor station work in a parallel way and these pressure-boosting stations in the pipeline are series connected. Considering the characteristic of gas pipelines, this paper develops two different UGFs to evaluate the system reliability. One(Model 1) establishes a system model from every compressor unit while the other(Model 2) considers the whole system as a combination of multi-state components. Besides, all the parameters of "weight" in UGFs are obtained from thermal-hydraulic models based on the actual engineering and"probability" from Monte Carlo simulation. The results show that the system reliabilities calculated by different UGFs are approximately equal. In addition, the demand of gas and the gas pipeline transportation system show a reverse trend. Because the number of parameters needed in Model 2 is far less than that needed in Model 1,Model 2 is simpler programming and faster solved.
文摘目的综合考虑B型超声(B-mode ultrasound,B-US)和对比增强超声(contrast-enhanced ultrasound,CEUS)双模态信息有助于提升乳腺肿瘤诊断的准确性,从而利于提高患者生存率。然而,目前大多数模型只关注B-US的特征提取,忽视了CEUS特征的学习和双模态信息的融合处理。为解决上述问题,提出了一个融合时空特征与时间约束的双模态乳腺肿瘤诊断模型(spatio-temporal feature and temporal-constrained model,STFTCM)。方法首先,基于双模态信息的数据特点,采用异构双分支网络学习B-US和CEUS包含的时空特征。然后,设计时间注意力损失函数引导CEUS分支关注造影剂流入病灶区的时间窗口,从该窗口期内提取CEUS特征。最后,借助特征融合模块实现双分支网络之间的横向连接,通过将B-US特征作为CEUS分支补充信息的方式,完成双模态特征融合。结果在收集到的数据集上进行对比实验,STFTCM预测的正确率、敏感性、宏平均F1和AUC(area under the curve)指标均表现优秀,其中预测正确率达88.2%,领先于其他先进模型。消融实验中,时间注意力约束将模型预测正确率提升5.8%,特征融合使得模型诊断正确率相较于单分支模型至少提升2.9%。结论本文提出的STFTCM能有效地提取并融合处理B-US和CEUS双模态信息,给出准确的诊断结果。同时,时间注意力约束和特征融合模块可以显著地提升模型性能。
