Phase-matching quantum-key distribution(PM-QKD)has achieved significant results in various practical applications.However,real-time communication requires dynamic adjustment and optimization of key parameters during c...Phase-matching quantum-key distribution(PM-QKD)has achieved significant results in various practical applications.However,real-time communication requires dynamic adjustment and optimization of key parameters during communication.In this letter,we predict the PM-QKD parameters using nature-inspired algorithms(NIAs).The results are obtained from an exhaustive traversal algorithm(ETA),which serves as a benchmark.We mainly study the parameter optimization effects of the two NIAs:ant colony optimization(ACO)and the genetic algorithm(GA).The configuration of the inherent parameters of these algorithms in the decoy-state PM-QKD is also discussed.The simulation results indicate that the parameters obtained by the ACO exhibit superior convergence and stability,whereas the GA results are relatively scattered.Nevertheless,more than 97%of the key rates predicted by both algorithms are highly consistent with the optimal key rate.Moreover,the relative error of the key rates remained below 10%.Furthermore,NIAs maintain power consumption below 8 W and require three orders of magnitude less computing time than ETA.展开更多
The development of a selective catalyst for the conversion of biomass and plastics into H2by steam reforming can combat the energy crisis and global warming.In this work,support Ni-Fe-Ca/H-Al bifunctional catalysts we...The development of a selective catalyst for the conversion of biomass and plastics into H2by steam reforming can combat the energy crisis and global warming.In this work,support Ni-Fe-Ca/H-Al bifunctional catalysts were prepared by loading Ni and Fe into pretreatment CaO/Al_(2)O_(3)(Ca/H-Al)carriers and showed high catalytic activity for the steam reforming of biomass and plastic.Moreover,the idea of bidirectional degradation was exploited to strengthen the pyrolysis of plastic with a high H/C and biomass with a high O/C.Interestingly,the products presented high H2selective(1302.10 m L/g)and low CO_(2)yield(120.23 m L/g)in 7Ni-5Fe-Ca/H-Al(2:4)catalyst compared with current reports.Here,the abundant oxygen vacancies(Ov)in the H-Al carrier exhibited an electron-deficient nature,providing active sites for anchoring Ni O.Meanwhile,Ni O interacted with Ca_(2)Fe_(2)O_(5)to produce more defective Ovsites,which stabilized the NiO particles in the 7Ni-5Fe-Ca/H-Al(2:4)catalyst,and the interaction between the catalyst and the carrier was enhanced,leading to the reduction of weakly basic sites,this property promoted the strong adsorption of CO_(2)and H2O by the catalyst,contributing to the enhancement of efficient steam conversion and the promotion of conversion of by-products to H2.Notably,7Ni-5Fe-Ca/H-Al(2:4)catalysts maintained structural integrity after regeneration and exhibited excellent regenerability in H2selection and CO_(2)adsorption.The work provides a new idea for the study of efficient H2production from steam reforming of biomass and plastics.展开更多
Nowadays, asphalt road has dominated highways around the world. Among various defects of asphalt road, crackshave been paid more attention, since cracks often cause major engineering and personnel safety incidents. Cu...Nowadays, asphalt road has dominated highways around the world. Among various defects of asphalt road, crackshave been paid more attention, since cracks often cause major engineering and personnel safety incidents. Currentmanual crack inspection methods are time-consuming and labor-intensive, and most segmentation methods cannot detect cracks at the pixel level. This paper proposes an intelligent segmentation and measurement model basedon the modified Mask R-CNN algorithm to automatically and accurately detect asphalt road cracks. The modelproposed in this paper mainly includes a convolutional neural network (CNN), an optimized region proposalnetwork (RPN), a region of interest (RoI) Align layer, a candidate area classification network and a Mask branch offully convolutional network (FCN). The ratio and size of anchors in the RPN are adjusted to improve the accuracyand efficiency of segmentation. Soft non-maximum suppression (Soft-NMS) algorithm is developed to improvethe segmentation accuracy. A dataset including 8,689 images (512× 512 pixels) of asphalt cracks is established andthe road crack is manually marked. Transfer learning is used to initialize the model parameters in the trainingprocess. To optimize the model training parameters, multiple comparison experiments are performed, and the testresults show that the mean average precision (mAP) value and F1-score of the optimal trained model are 0.952 and0.949. Subsequently, the robustness verification test and comparative test of the trained model are conducted andthe topological features of the crack are extracted. Then, the damage area, length and average width of the crackare measured automatically and accurately at pixel level. More importantly, this paper develops an automatic crackdetection platform for asphalt roads to automatically extract the number, area, length and average width of cracks,which can significantly improve the crack detection efficiency for the road maintenance industry.展开更多
Our previous study has shown that the transcription factor Krüppel-like factor 7(KLF7) promotes peripheral nerve regeneration and motor function recovery after spinal cord injury.KLF7 also participates in traumat...Our previous study has shown that the transcription factor Krüppel-like factor 7(KLF7) promotes peripheral nerve regeneration and motor function recovery after spinal cord injury.KLF7 also participates in traumatic brain injury,but its regulatory mechanisms remain poorly understood.In the present study,an HT22 cell model of traumatic brain injury was established by stretch injury and oxygenglucose deprivation.These cells were then transfected with an adeno-associated virus carrying KLF7(AAV-KLF7).The results revealed that,after stretch injury and oxygen-glucose deprivation,KLF7 greatly reduced apoptosis,activated caspase-3 and lactate dehydrogenase,downregulated the expression of the apoptotic markers B-cell lymphoma 2(Bcl-2)-associated X protein(Bax) and cleaved caspase-3,and increased the expression of βIII-tubulin and the antiapoptotic marker Bcl-2.Furthermore,KLF7 overexpression upregulated Janus kinase 2(JAK2) and signal transducer and activator of transcription 3(STAT3) phosphorylation in HT22 cells treated by stretch injury and oxygenglucose deprivation.Immunoprecipitation assays revealed that KLF7 directly participated in the phosphorylation of STAT3.In addition,treatment with AG490,a selective inhibitor of JAK2/STAT3,weakened the protective effects of KLF7.A mouse controlled cortical impact model of traumatic brain injury was then established.At 30 minutes before modeling,AAV-KLF7 was injected into the ipsilateral lateral ventricle.The protein and m RNA levels of KLF7 in the hippocampus were increased at 1 day after injury and recovered to normal levels at 3 days after injury.KLF7 reduced ipsilateral hippocampal atrophy,decreased the injured cortex volume,downregulated Bax and cleaved caspase-3 expression,and increased the number of 5-bromo-2'-deoxyuridine-positive neurons and Bcl-2 protein expression.Moreover,KLF7 transfection greatly enhanced the phosphorylation of JAK2 and STAT3 in the ipsilateral hippocampus.These results suggest that KLF7 may protect hippocampal neurons after traumatic brain injury through activation of the JAK2/STAT3 signaling pathway.The study was approved by the Institutional Review Board of Mudanjiang Medical University,China(approval No.mdjyxy-2018-0012) on March 6,2018.展开更多
Rational design of high-performance electrocatalysts for hydrogen evolution reaction(HER)is vital for future renewable energy systems.The incorporation of foreign metal ions into catalysts can be an effective approach...Rational design of high-performance electrocatalysts for hydrogen evolution reaction(HER)is vital for future renewable energy systems.The incorporation of foreign metal ions into catalysts can be an effective approach to optimize its performance.However,there is a lack of systematic theoretical studies to reveal the quantitative relationships at the electronic level.Here,we develop a multi-level screening methodology to search for highly stable and active dopants for CoP catalysts.The density functional theory(DFT)calculations and symbolic regression(SR)were performed to investigate the relationship between the adsorption free energy(ΔG_(H^(*)))and 10 electronic parameters.The mathematic formulas derived from SR indicate that the difference of work function(ΔΦ)between doped metal and the acceptor plays the most important role in regulatingΔG_(H^(*)),followed by the d-band center(d-BC)of doped system.The descriptor of HER can be expressed asΔG_(H^(*))=1.59×√|0.188ΔΦ+d BC+0.120|1/2-0.166 with a high determination coefficient(R^(2)=0.807).Consistent with the theoretical prediction,experimental results show that the Al-CoP delivers superior electrocatalytic HER activity with a low overpotential of75 m V to drive a current density of 10 mA cm^(-2),while the overpotentials for undoped CoP,Mo-CoP,and V-CoP are 206,134,and 83 m V,respectively.The current work proves that theΔΦis the most significant regulatory parameter ofΔG_(H^(*))for ion-doped electrocatalysts.This finding can drive the discovery of high-performance ion-doped electrocatalysts,which is crucial for electrocatalytic water splitting.展开更多
The crack is a common pavement failure problem.A lack of periodic maintenance will result in extending the cracks and damage the pavement,which will affect the normal use of the road.Therefore,it is significant to est...The crack is a common pavement failure problem.A lack of periodic maintenance will result in extending the cracks and damage the pavement,which will affect the normal use of the road.Therefore,it is significant to establish an efficient intelligent identification model for pavement cracks.The neural network is a method of simulating animal nervous systems using gradient descent to predict results by learning a weight matrix.It has been widely used in geotechnical engineering,computer vision,medicine,and other fields.However,there are three major problems in the application of neural networks to crack identification.There are too few layers,extracted crack features are not complete,and the method lacks the efficiency to calculate the whole picture.In this study,a fully convolutional neural network based on ResNet-101 is used to establish an intelligent identification model of pavement crack regions.This method,using a convolutional layer instead of a fully connected layer,realizes full convolution and accelerates calculation.The region proposals come from the feature map at the end of the base network,which avoids multiple computations of the same picture.Online hard example mining and data-augmentation techniques are adopted to improve the model’s recognition accuracy.We trained and tested Concrete Crack Images for Classification(CCIC),which is a public dataset collected using smartphones,and the Crack Image Database(CIDB),which was automatically collected using vehicle-mounted charge-coupled device cameras,with identification accuracy reaching 91.4%and 86.4%,respectively.The proposed model has a higher recognition accuracy and recall rate than Faster RCNN and different depth models,and can extract more complete and accurate crack features in CIDB.We also analyzed translation processing,fuzzy,scaling,and distorted images.The proposed model shows a strong robustness and stability,and can automatically identify image cracks of different forms.It has broad application prospects in practical engineering problems.展开更多
Improving the response of sensors is often hindered by inadequate molding effects and complex manufacturing processes. Here, combining a simple magnetic-field-orientation and nano-imprinting process, a micropillar arr...Improving the response of sensors is often hindered by inadequate molding effects and complex manufacturing processes. Here, combining a simple magnetic-field-orientation and nano-imprinting process, a micropillar arrayed sensor was successfully fabricated, meanwhile, the boron nitride nanosheets (BNNS) were oriented in the polymer matrix. Due to the strain confinement effect, the outputted voltage of m-BNNS/PDMS composite film (SABNNS) demonstrated an improvement of 115.5% compared to the film sample with randomly dispersed nanoparticles. And the device showed a high sensitivity and rapid response capability to human motion. Furthermore, the oriented arrangement of m-BNNS and the enlarged heat dis-sipation area of the micropillar array contribute to the optimized thermal conductivity of the device.展开更多
4D printing polymeric biomaterials can change their morphology or performance in response to stimuli from the external environment,compensating for the shortcomings of traditional 3D-printed static structures.This pap...4D printing polymeric biomaterials can change their morphology or performance in response to stimuli from the external environment,compensating for the shortcomings of traditional 3D-printed static structures.This paper provides a systematic overview of 4D printing polymeric biomaterials for tissue regeneration and provides an indepth discussion of the principles of these materials,including various smart properties,unique deformation mechanisms under stimulation conditions,and so on.A series of typical polymeric biomaterials and their composites are introduced from structural design and preparation methods,and their applications in tissue regeneration are discussed.Finally,the development prospect of 4D printing polymeric biomaterials is envi-sioned,aiming to provide innovative ideas and new perspectives for their more efficient and convenient application in tissue regeneration.展开更多
The durability of proton exchange membrane fuel cells (PEMFC) is an important issue that restricts their large-scale application. To improve their reliability during use, this paper proposes a short-term performance d...The durability of proton exchange membrane fuel cells (PEMFC) is an important issue that restricts their large-scale application. To improve their reliability during use, this paper proposes a short-term performance degradation prediction model using particle swarm optimization (PSO) to optimize the gate recurrent unit (GRU). After training using only the data from the first 300 h, good prediction accuracy can be achieved. Compared with the traditional GRU algorithm, the proposed prediction method reduces the root mean square error (RMSE) and mean absolute error (MAE) of the prediction results by 44.8 % and 35.1 %, respectively. It avoids the problem of low accuracy in predicting performance during the temporary recovery phase in traditional GRU models, which is of great significance for the health management of PEMFC.展开更多
基金supported by the State Key Laboratory of Information Photonics and Optical Communications(Beijing University of Posts and Telecommunications)No.IPOC2021ZT10BUPT Excellent Ph.D.Students Foundation(Grant No.CX2023207)the BUPT innovation and entrepreneurship support program No.2024-YC-A188。
文摘Phase-matching quantum-key distribution(PM-QKD)has achieved significant results in various practical applications.However,real-time communication requires dynamic adjustment and optimization of key parameters during communication.In this letter,we predict the PM-QKD parameters using nature-inspired algorithms(NIAs).The results are obtained from an exhaustive traversal algorithm(ETA),which serves as a benchmark.We mainly study the parameter optimization effects of the two NIAs:ant colony optimization(ACO)and the genetic algorithm(GA).The configuration of the inherent parameters of these algorithms in the decoy-state PM-QKD is also discussed.The simulation results indicate that the parameters obtained by the ACO exhibit superior convergence and stability,whereas the GA results are relatively scattered.Nevertheless,more than 97%of the key rates predicted by both algorithms are highly consistent with the optimal key rate.Moreover,the relative error of the key rates remained below 10%.Furthermore,NIAs maintain power consumption below 8 W and require three orders of magnitude less computing time than ETA.
基金the National Natural Science of China(21968037)the Reserve Program for Young and Middle-aged Academic and Technical Leaders in Yunnan Province(202205AC160031)+1 种基金the Research Innovation Project of Yunnan University for Graduate Students on Exemption,the Highlevel Talent Promotion and Training Project of Kunming(2022SCP003)advanced analysis and measurement center of Yunnan university for the sample testing service。
文摘The development of a selective catalyst for the conversion of biomass and plastics into H2by steam reforming can combat the energy crisis and global warming.In this work,support Ni-Fe-Ca/H-Al bifunctional catalysts were prepared by loading Ni and Fe into pretreatment CaO/Al_(2)O_(3)(Ca/H-Al)carriers and showed high catalytic activity for the steam reforming of biomass and plastic.Moreover,the idea of bidirectional degradation was exploited to strengthen the pyrolysis of plastic with a high H/C and biomass with a high O/C.Interestingly,the products presented high H2selective(1302.10 m L/g)and low CO_(2)yield(120.23 m L/g)in 7Ni-5Fe-Ca/H-Al(2:4)catalyst compared with current reports.Here,the abundant oxygen vacancies(Ov)in the H-Al carrier exhibited an electron-deficient nature,providing active sites for anchoring Ni O.Meanwhile,Ni O interacted with Ca_(2)Fe_(2)O_(5)to produce more defective Ovsites,which stabilized the NiO particles in the 7Ni-5Fe-Ca/H-Al(2:4)catalyst,and the interaction between the catalyst and the carrier was enhanced,leading to the reduction of weakly basic sites,this property promoted the strong adsorption of CO_(2)and H2O by the catalyst,contributing to the enhancement of efficient steam conversion and the promotion of conversion of by-products to H2.Notably,7Ni-5Fe-Ca/H-Al(2:4)catalysts maintained structural integrity after regeneration and exhibited excellent regenerability in H2selection and CO_(2)adsorption.The work provides a new idea for the study of efficient H2production from steam reforming of biomass and plastics.
基金This research was funded by the National Key Research and Development Program of China(No.2017YFC1501204)the National Natural Science Foundation of China(No.51678536)+4 种基金the Guangdong Innovative and Entrepreneurial Research Team Program(2016ZT06N340)the Program for Science and Technology Innovation Talents in Universities of Henan Province(Grant No.19HASTIT043)the Outstanding Young Talent Research Fund of Zhengzhou University(1621323001)the Program for Innovative Research Team(in Science and Technology)in University of Henan Province(18IRTSTHN007)the Research on NonDestructive Testing(NDT)and Rapid Evaluation Technology for Grouting Quality of Prestressed Ducts(Contract No.HG-GCKY-01-002).The authors would like to thank for these financial supports.
文摘Nowadays, asphalt road has dominated highways around the world. Among various defects of asphalt road, crackshave been paid more attention, since cracks often cause major engineering and personnel safety incidents. Currentmanual crack inspection methods are time-consuming and labor-intensive, and most segmentation methods cannot detect cracks at the pixel level. This paper proposes an intelligent segmentation and measurement model basedon the modified Mask R-CNN algorithm to automatically and accurately detect asphalt road cracks. The modelproposed in this paper mainly includes a convolutional neural network (CNN), an optimized region proposalnetwork (RPN), a region of interest (RoI) Align layer, a candidate area classification network and a Mask branch offully convolutional network (FCN). The ratio and size of anchors in the RPN are adjusted to improve the accuracyand efficiency of segmentation. Soft non-maximum suppression (Soft-NMS) algorithm is developed to improvethe segmentation accuracy. A dataset including 8,689 images (512× 512 pixels) of asphalt cracks is established andthe road crack is manually marked. Transfer learning is used to initialize the model parameters in the trainingprocess. To optimize the model training parameters, multiple comparison experiments are performed, and the testresults show that the mean average precision (mAP) value and F1-score of the optimal trained model are 0.952 and0.949. Subsequently, the robustness verification test and comparative test of the trained model are conducted andthe topological features of the crack are extracted. Then, the damage area, length and average width of the crackare measured automatically and accurately at pixel level. More importantly, this paper develops an automatic crackdetection platform for asphalt roads to automatically extract the number, area, length and average width of cracks,which can significantly improve the crack detection efficiency for the road maintenance industry.
基金supported by the National Natural Science Foundation of China,No.81870977 (to YW)the Natural Science Foundation of Heilongjiang of China,No.H2018068 (to WYL)+3 种基金the Basic Research Operating Expenses Program of Heilongjiang Provincial Universities of China,No.2019-KYYWFMY-0018 (to WYL)the Graduate Innovative Research Projects of Mudanjiang Medical College of China,No.YJSCX-MY22 (to DM)Key projects of Education Department of Hebei Province of China,No.ZD2020178 (to XMF)Hebei Key Laboratory of Nerve Injury and Repair of China (to XMF)。
文摘Our previous study has shown that the transcription factor Krüppel-like factor 7(KLF7) promotes peripheral nerve regeneration and motor function recovery after spinal cord injury.KLF7 also participates in traumatic brain injury,but its regulatory mechanisms remain poorly understood.In the present study,an HT22 cell model of traumatic brain injury was established by stretch injury and oxygenglucose deprivation.These cells were then transfected with an adeno-associated virus carrying KLF7(AAV-KLF7).The results revealed that,after stretch injury and oxygen-glucose deprivation,KLF7 greatly reduced apoptosis,activated caspase-3 and lactate dehydrogenase,downregulated the expression of the apoptotic markers B-cell lymphoma 2(Bcl-2)-associated X protein(Bax) and cleaved caspase-3,and increased the expression of βIII-tubulin and the antiapoptotic marker Bcl-2.Furthermore,KLF7 overexpression upregulated Janus kinase 2(JAK2) and signal transducer and activator of transcription 3(STAT3) phosphorylation in HT22 cells treated by stretch injury and oxygenglucose deprivation.Immunoprecipitation assays revealed that KLF7 directly participated in the phosphorylation of STAT3.In addition,treatment with AG490,a selective inhibitor of JAK2/STAT3,weakened the protective effects of KLF7.A mouse controlled cortical impact model of traumatic brain injury was then established.At 30 minutes before modeling,AAV-KLF7 was injected into the ipsilateral lateral ventricle.The protein and m RNA levels of KLF7 in the hippocampus were increased at 1 day after injury and recovered to normal levels at 3 days after injury.KLF7 reduced ipsilateral hippocampal atrophy,decreased the injured cortex volume,downregulated Bax and cleaved caspase-3 expression,and increased the number of 5-bromo-2'-deoxyuridine-positive neurons and Bcl-2 protein expression.Moreover,KLF7 transfection greatly enhanced the phosphorylation of JAK2 and STAT3 in the ipsilateral hippocampus.These results suggest that KLF7 may protect hippocampal neurons after traumatic brain injury through activation of the JAK2/STAT3 signaling pathway.The study was approved by the Institutional Review Board of Mudanjiang Medical University,China(approval No.mdjyxy-2018-0012) on March 6,2018.
基金Financial support from the National Natural Science Foundation of China(21676216)the Special project of Shaanxi Provincial Education Department(20JC034)+1 种基金GHfund B(202202022563)Hefei Advanced Computing Center。
文摘Rational design of high-performance electrocatalysts for hydrogen evolution reaction(HER)is vital for future renewable energy systems.The incorporation of foreign metal ions into catalysts can be an effective approach to optimize its performance.However,there is a lack of systematic theoretical studies to reveal the quantitative relationships at the electronic level.Here,we develop a multi-level screening methodology to search for highly stable and active dopants for CoP catalysts.The density functional theory(DFT)calculations and symbolic regression(SR)were performed to investigate the relationship between the adsorption free energy(ΔG_(H^(*)))and 10 electronic parameters.The mathematic formulas derived from SR indicate that the difference of work function(ΔΦ)between doped metal and the acceptor plays the most important role in regulatingΔG_(H^(*)),followed by the d-band center(d-BC)of doped system.The descriptor of HER can be expressed asΔG_(H^(*))=1.59×√|0.188ΔΦ+d BC+0.120|1/2-0.166 with a high determination coefficient(R^(2)=0.807).Consistent with the theoretical prediction,experimental results show that the Al-CoP delivers superior electrocatalytic HER activity with a low overpotential of75 m V to drive a current density of 10 mA cm^(-2),while the overpotentials for undoped CoP,Mo-CoP,and V-CoP are 206,134,and 83 m V,respectively.The current work proves that theΔΦis the most significant regulatory parameter ofΔG_(H^(*))for ion-doped electrocatalysts.This finding can drive the discovery of high-performance ion-doped electrocatalysts,which is crucial for electrocatalytic water splitting.
基金funded by the National Key Research and Development Program of China(No.2017YFC1501200)the National Natural Science Foundation of China(Nos.51678536,41404096)+2 种基金supported by Department of education’s Production-Study-Research combined innovation Funding-“Blue fire plan(Huizhou)”(CXZJHZ01742)the Program for Science and Technology Innovation Talents in Universities of Henan Province(Grant No.19HASTIT043)the Outstanding Young Talent Research Fund of Zhengzhou University(1621323001).
文摘The crack is a common pavement failure problem.A lack of periodic maintenance will result in extending the cracks and damage the pavement,which will affect the normal use of the road.Therefore,it is significant to establish an efficient intelligent identification model for pavement cracks.The neural network is a method of simulating animal nervous systems using gradient descent to predict results by learning a weight matrix.It has been widely used in geotechnical engineering,computer vision,medicine,and other fields.However,there are three major problems in the application of neural networks to crack identification.There are too few layers,extracted crack features are not complete,and the method lacks the efficiency to calculate the whole picture.In this study,a fully convolutional neural network based on ResNet-101 is used to establish an intelligent identification model of pavement crack regions.This method,using a convolutional layer instead of a fully connected layer,realizes full convolution and accelerates calculation.The region proposals come from the feature map at the end of the base network,which avoids multiple computations of the same picture.Online hard example mining and data-augmentation techniques are adopted to improve the model’s recognition accuracy.We trained and tested Concrete Crack Images for Classification(CCIC),which is a public dataset collected using smartphones,and the Crack Image Database(CIDB),which was automatically collected using vehicle-mounted charge-coupled device cameras,with identification accuracy reaching 91.4%and 86.4%,respectively.The proposed model has a higher recognition accuracy and recall rate than Faster RCNN and different depth models,and can extract more complete and accurate crack features in CIDB.We also analyzed translation processing,fuzzy,scaling,and distorted images.The proposed model shows a strong robustness and stability,and can automatically identify image cracks of different forms.It has broad application prospects in practical engineering problems.
基金supported by the National Natural Science Foundation of China(52175544 and 52172098)the Key R&D Program of Shannxi Province(2022GXLH-01-12,2023-GHZD-11 and 2023QCY-LL-26HZ)the Featured Research Base Project of Xi’an Science and Technology Bureau(23TSPT0001).
文摘Improving the response of sensors is often hindered by inadequate molding effects and complex manufacturing processes. Here, combining a simple magnetic-field-orientation and nano-imprinting process, a micropillar arrayed sensor was successfully fabricated, meanwhile, the boron nitride nanosheets (BNNS) were oriented in the polymer matrix. Due to the strain confinement effect, the outputted voltage of m-BNNS/PDMS composite film (SABNNS) demonstrated an improvement of 115.5% compared to the film sample with randomly dispersed nanoparticles. And the device showed a high sensitivity and rapid response capability to human motion. Furthermore, the oriented arrangement of m-BNNS and the enlarged heat dis-sipation area of the micropillar array contribute to the optimized thermal conductivity of the device.
基金supported by the National Key R&D Program(Grant No.2022YFB3804403)the National Natural Science Foundation of China(Grant Nos.92468106 and 82402533)+3 种基金the Natural Science Foundation of Guangdong Province in China(Grant No.2024B1515040018)the Shenzhen Fundamental Research Foundation(Grant Nos.JCYJ20220818101613028,and JSGGKQTD20210831174330015)the Shenzhen Medical Research Fund(Grant No.B2402016)the Shenzhen Science and Technology Program(JCYJ20240813153012017).
文摘4D printing polymeric biomaterials can change their morphology or performance in response to stimuli from the external environment,compensating for the shortcomings of traditional 3D-printed static structures.This paper provides a systematic overview of 4D printing polymeric biomaterials for tissue regeneration and provides an indepth discussion of the principles of these materials,including various smart properties,unique deformation mechanisms under stimulation conditions,and so on.A series of typical polymeric biomaterials and their composites are introduced from structural design and preparation methods,and their applications in tissue regeneration are discussed.Finally,the development prospect of 4D printing polymeric biomaterials is envi-sioned,aiming to provide innovative ideas and new perspectives for their more efficient and convenient application in tissue regeneration.
基金supported in part by the Research on Key Technologies of Low Temperature and Long Life Fuel Cells,under Grant 20220301010Gxin part by Qingdao postdoctoral support project under Grant QDBSH20220202020Qingdao Natural Science Foundation under Grant 23-2-1-110-zyyd-jch,Shandong Natural Science Foundation under Grants ZR2023QE208.
文摘The durability of proton exchange membrane fuel cells (PEMFC) is an important issue that restricts their large-scale application. To improve their reliability during use, this paper proposes a short-term performance degradation prediction model using particle swarm optimization (PSO) to optimize the gate recurrent unit (GRU). After training using only the data from the first 300 h, good prediction accuracy can be achieved. Compared with the traditional GRU algorithm, the proposed prediction method reduces the root mean square error (RMSE) and mean absolute error (MAE) of the prediction results by 44.8 % and 35.1 %, respectively. It avoids the problem of low accuracy in predicting performance during the temporary recovery phase in traditional GRU models, which is of great significance for the health management of PEMFC.
