Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are...Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.展开更多
Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay di...Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.展开更多
Deep sea,with rich oil,gas,and mineral resources,plays an increasingly crucial role in scientific and industrial realms.However,the highly corrosive feature of deep sea hinders further exploration and development,whic...Deep sea,with rich oil,gas,and mineral resources,plays an increasingly crucial role in scientific and industrial realms.However,the highly corrosive feature of deep sea hinders further exploration and development,which requires metal materials with robust corrosion resistance.This review covers an in-depth and all-around overview of the up-to-date advances in corrosion and protection of metals in deep-sea environment.Firstly,the unique characteristics of deep-sea environment are summarized in detail.Subsequently,the corrosion performances of metals in both in situ and simulated deep-sea environments are illustrated systematically.Furthermore,corrosion prevent strategies of metals,including sacrificial anode protection,organic coatings,as well as coatings achieved by physical vapor deposition(PVD coatings),are highlighted.Finally,we outline current challenges and development trends of corrosion and protection of metals in deep-sea environment in the future.The purpose of this review is not only to summarize the recent progress on metal corrosion and protection in deep sea,but also to aid us in understanding them more comprehensively and deeply in a short time,so as to boost their fast development.展开更多
Titanium alloy has the advantages of high strength,strong corrosion resistance,excellent high and low temperature mechanical properties,etc.,and is widely used in aerospace,shipbuilding,weapons and equipment,and other...Titanium alloy has the advantages of high strength,strong corrosion resistance,excellent high and low temperature mechanical properties,etc.,and is widely used in aerospace,shipbuilding,weapons and equipment,and other fields.In recent years,with the continuous increase in demand for medium-thick plate titanium alloys,corresponding welding technologies have also continued to develop.Therefore,this article reviews the research progress of deep penetration welding technology for medium-thick plate titanium alloys,mainly covering traditional arc welding,high-energy beam welding,and other welding technologies.Among many methods,narrow gap welding,hybrid welding,and external energy field assistance welding all contribute to improving the welding efficiency and quality of medium-thick plate titanium alloys.Finally,the development trend of deep penetration welding technology for mediumthick plate titanium alloys is prospected.展开更多
In the article“Deep Learning-Enhanced Brain Tumor Prediction via Entropy-Coded BPSO in CIELAB Color Space”by Mudassir Khalil,Muhammad Imran Sharif,Ahmed Naeem,Muhammad Umar Chaudhry,Hafiz Tayyab Rauf,Adham E.Ragab C...In the article“Deep Learning-Enhanced Brain Tumor Prediction via Entropy-Coded BPSO in CIELAB Color Space”by Mudassir Khalil,Muhammad Imran Sharif,Ahmed Naeem,Muhammad Umar Chaudhry,Hafiz Tayyab Rauf,Adham E.Ragab Computers,Materials&Continua,2023,Vol.77,No.2,pp.2031–2047.DOI:10.32604/cmc.2023.043687,URL:https://www.techscience.com/cmc/v77n2/54831,there was an error regarding the affiliation for the author Hafiz Tayyab Rauf.Instead of“Centre for Smart Systems,AI and Cybersecurity,Staffordshire University,Stoke-on-Trent,ST42DE,UK”,the affiliation should be“Independent Researcher,Bradford,BD80HS,UK”.展开更多
Bearing is an indispensable key component in mechanical equipment,and its working state is directly related to the stability and safety of the whole equipment.In recent years,with the rapid development of artificial i...Bearing is an indispensable key component in mechanical equipment,and its working state is directly related to the stability and safety of the whole equipment.In recent years,with the rapid development of artificial intelligence technology,especially the breakthrough of deep learning technology,it provides a new idea for bearing fault diagnosis.Deep learning can automatically learn features from a large amount of data,has a strong nonlinear modeling ability,and can effectively solve the problems existing in traditional methods.Aiming at the key problems in bearing fault diagnosis,this paper studies the fault diagnosis method based on deep learning,which not only provides a new solution for bearing fault diagnosis but also provides a reference for the application of deep learning in other mechanical fault diagnosis fields.展开更多
BACKGROUND Hepatocellular carcinoma(HCC)is a prevalent and life-threatening cancer with increasing incidence worldwide.High Ki-67 risk stratification is closely associated with higher recurrence rates and worse outcom...BACKGROUND Hepatocellular carcinoma(HCC)is a prevalent and life-threatening cancer with increasing incidence worldwide.High Ki-67 risk stratification is closely associated with higher recurrence rates and worse outcomes following curative therapies in patients with HCC.However,the performance of radiomic and deep transfer learning(DTL)models derived from biparametric magnetic resonance imaging(bpMRI)in predicting Ki-67 risk stratification and recurrence-free survival(RFS)in patients with HCC remains limited.AIM To develop a nomogram model integrating bpMRI-based radiomic and DTL signatures for predicting Ki-67 risk stratification and RFS in patients with HCC.METHODS This study included 198 patients with histopathologically confirmed HCC who underwent preoperative bpMRI.Ki-67 risk stratification was categorized as high(>20%)or low(≤20%)according to immunohistochemical staining.Radiomic and DTL signatures were extracted from the T2-weighted and arterial-phase images and combined through a random forest algorithm to establish radiomic and DTL models,respectively.Multivariate regression analysis identified clinical risk factors for high Ki-67 risk stratification,and a predictive nomogram model was developed.RESULTS A nonsmooth margin and the absence of an enhanced capsule were independent factors for high Ki-67 risk stratification.The area under the curve(AUC)of the clinical model was 0.77,while those of the radiomic and DTL models were 0.81 and 0.87,respectively,for the prediction of high Ki-67 risk stratification,and the nomogram model achieved a better AUC of 0.92.The median RFS times for patients with high and low Ki-67 risk stratification were 33.00 months and 66.73 months,respectively(P<0.001).Additionally,patients who were predicted to have high Ki-67 risk stratification by the nomogram model had a lower median RFS than those who were predicted to have low Ki-67 risk stratification(33.53 vs 66.74 months,P=0.007).CONCLUSION Our developed nomogram model demonstrated good performance in predicting Ki-67 risk stratification and predicting survival outcomes in patients with HCC.展开更多
Gels and conductive polymer composites,including hydrogen bonds(HBs),have emerged as promising materials for electro-magnetic wave(EMW)absorption across various applications.However,the relationship between conduction...Gels and conductive polymer composites,including hydrogen bonds(HBs),have emerged as promising materials for electro-magnetic wave(EMW)absorption across various applications.However,the relationship between conduction loss in EMW-absorbing materials and charge transfer in HB remains to be fully understood.In this study,we developed a series of deep eutectic gels to fine-tune the quantity of HB by adjusting the molar ratio of choline chloride(ChCl)and ethylene glycol(EG).Owing to the unique properties of deep eutectic gels,the effects of magnetic loss and polarization loss on EMW attenuation can be disregarded.Our results indicate that the quantity of HB initially increases and then decreases with the introduction of EG,with HB-induced conductive loss following similar pat-terns.At a ChCl and EG molar ratio of 2.4,the gel labeled G22-CE2.4 exhibited the best EMW absorption performance,characterized by an effective absorption bandwidth of 8.50 GHz and a thickness of 2.54 mm.This superior performance is attributed to the synergistic ef-fects of excellent conductive loss and impedance matching generated by the optimal number of HB.This work elucidates the role of HB in dielectric loss for the first time and provides valuable insights into the optimal design of supramolecular polymer absorbers.展开更多
The loaded rock experiences multiple stages of deformation.It starts with the formation of microcracks at low stresses(crack initiation,CI)and then transitions into unstable crack propagation(crack damage,CD)near the ...The loaded rock experiences multiple stages of deformation.It starts with the formation of microcracks at low stresses(crack initiation,CI)and then transitions into unstable crack propagation(crack damage,CD)near the ultimate strength.In this study,both the acoustic emission method(AEM)and the ultrasonic testing method(UTM)were used to examine the characteristics of AE parameters(b-value,peak frequency,frequency-band energy ratio,and fractal dimension)and ultrasonic(ULT)properties(velocity,amplitude,energy attenuation,and scattering attenuation)of bedded shale at CI,CD,and ultimate strength.The comparison involved analyzing the strain-based method(SBM),AEM,and UTM to determine the thresholds for damage stress.A fuzzy comprehensive evaluation model(FCEM)was created to describe the damage thresholds and hazard assessment.The results indicate that the optimal AE and ULT parameters for identifying CI and CD stress are ringing count,ultrasonic amplitude,energy attenuation,and scattering attenuation of the S-wave.Besides,damage thresholds were detected earlier by AE monitoring,ranging from 3 MPa to 10 MPa.CI and CD identified by UTM occurred later than SBM and AEM,and were in the range of 12 MPa.The b-value,peak frequency,energy ratio in the low-frequency band(0e62.5 kHz),correlation dimension,and sandbox dimension showed low values at the peak stress,while the energy ratio in a moderate-frequency band(187.5e281.25 kHz)and amplitude showed high values.The successful application of FCEM to laboratory testing of shales has demonstrated its ability to quantitatively identify AE/ULT precursors of seismic hazards associated with rock failure.展开更多
基金supported by the Ministry of Science and Technology of China,No.2020AAA0109605(to XL)Meizhou Major Scientific and Technological Innovation PlatformsProjects of Guangdong Provincial Science & Technology Plan Projects,No.2019A0102005(to HW).
文摘Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.
文摘Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.
基金the National Key R&D Program of China(No.2022YFB3808800)the National Natural Science Foundation of China(Nos.52301406 and 52375219)+2 种基金the Natural Science Foundation of Zhejiang Province(No.LR21E050001)the China Postdoctoral Science Foundation(No.2023M733600)the Ningbo Natural Science Foundation(No.2023J329).
文摘Deep sea,with rich oil,gas,and mineral resources,plays an increasingly crucial role in scientific and industrial realms.However,the highly corrosive feature of deep sea hinders further exploration and development,which requires metal materials with robust corrosion resistance.This review covers an in-depth and all-around overview of the up-to-date advances in corrosion and protection of metals in deep-sea environment.Firstly,the unique characteristics of deep-sea environment are summarized in detail.Subsequently,the corrosion performances of metals in both in situ and simulated deep-sea environments are illustrated systematically.Furthermore,corrosion prevent strategies of metals,including sacrificial anode protection,organic coatings,as well as coatings achieved by physical vapor deposition(PVD coatings),are highlighted.Finally,we outline current challenges and development trends of corrosion and protection of metals in deep-sea environment in the future.The purpose of this review is not only to summarize the recent progress on metal corrosion and protection in deep sea,but also to aid us in understanding them more comprehensively and deeply in a short time,so as to boost their fast development.
基金financially supported by the Key Research and Development Program of Ningbo(Grant No.2023Z098)Natural Science Foundation of Inner Mongolia(Grant No.2023MS05040)+1 种基金Shenyang Collaborative Innovation Center Project for Multiple Energy Fields Composite Processing of Special Materials(Grant No.JG210027)Shenyang Key Technology Special Project of The Open Competition Mechanism to Select the Best Solution(Grant Nos.2022210101000827,2022-0-43-048).
文摘Titanium alloy has the advantages of high strength,strong corrosion resistance,excellent high and low temperature mechanical properties,etc.,and is widely used in aerospace,shipbuilding,weapons and equipment,and other fields.In recent years,with the continuous increase in demand for medium-thick plate titanium alloys,corresponding welding technologies have also continued to develop.Therefore,this article reviews the research progress of deep penetration welding technology for medium-thick plate titanium alloys,mainly covering traditional arc welding,high-energy beam welding,and other welding technologies.Among many methods,narrow gap welding,hybrid welding,and external energy field assistance welding all contribute to improving the welding efficiency and quality of medium-thick plate titanium alloys.Finally,the development trend of deep penetration welding technology for mediumthick plate titanium alloys is prospected.
文摘In the article“Deep Learning-Enhanced Brain Tumor Prediction via Entropy-Coded BPSO in CIELAB Color Space”by Mudassir Khalil,Muhammad Imran Sharif,Ahmed Naeem,Muhammad Umar Chaudhry,Hafiz Tayyab Rauf,Adham E.Ragab Computers,Materials&Continua,2023,Vol.77,No.2,pp.2031–2047.DOI:10.32604/cmc.2023.043687,URL:https://www.techscience.com/cmc/v77n2/54831,there was an error regarding the affiliation for the author Hafiz Tayyab Rauf.Instead of“Centre for Smart Systems,AI and Cybersecurity,Staffordshire University,Stoke-on-Trent,ST42DE,UK”,the affiliation should be“Independent Researcher,Bradford,BD80HS,UK”.
文摘Bearing is an indispensable key component in mechanical equipment,and its working state is directly related to the stability and safety of the whole equipment.In recent years,with the rapid development of artificial intelligence technology,especially the breakthrough of deep learning technology,it provides a new idea for bearing fault diagnosis.Deep learning can automatically learn features from a large amount of data,has a strong nonlinear modeling ability,and can effectively solve the problems existing in traditional methods.Aiming at the key problems in bearing fault diagnosis,this paper studies the fault diagnosis method based on deep learning,which not only provides a new solution for bearing fault diagnosis but also provides a reference for the application of deep learning in other mechanical fault diagnosis fields.
基金Supported by Clinical Trials from the Third Affiliated Hospital of Soochow University,No.2024-156Changzhou Science and Technology Program,No.CJ20244017。
文摘BACKGROUND Hepatocellular carcinoma(HCC)is a prevalent and life-threatening cancer with increasing incidence worldwide.High Ki-67 risk stratification is closely associated with higher recurrence rates and worse outcomes following curative therapies in patients with HCC.However,the performance of radiomic and deep transfer learning(DTL)models derived from biparametric magnetic resonance imaging(bpMRI)in predicting Ki-67 risk stratification and recurrence-free survival(RFS)in patients with HCC remains limited.AIM To develop a nomogram model integrating bpMRI-based radiomic and DTL signatures for predicting Ki-67 risk stratification and RFS in patients with HCC.METHODS This study included 198 patients with histopathologically confirmed HCC who underwent preoperative bpMRI.Ki-67 risk stratification was categorized as high(>20%)or low(≤20%)according to immunohistochemical staining.Radiomic and DTL signatures were extracted from the T2-weighted and arterial-phase images and combined through a random forest algorithm to establish radiomic and DTL models,respectively.Multivariate regression analysis identified clinical risk factors for high Ki-67 risk stratification,and a predictive nomogram model was developed.RESULTS A nonsmooth margin and the absence of an enhanced capsule were independent factors for high Ki-67 risk stratification.The area under the curve(AUC)of the clinical model was 0.77,while those of the radiomic and DTL models were 0.81 and 0.87,respectively,for the prediction of high Ki-67 risk stratification,and the nomogram model achieved a better AUC of 0.92.The median RFS times for patients with high and low Ki-67 risk stratification were 33.00 months and 66.73 months,respectively(P<0.001).Additionally,patients who were predicted to have high Ki-67 risk stratification by the nomogram model had a lower median RFS than those who were predicted to have low Ki-67 risk stratification(33.53 vs 66.74 months,P=0.007).CONCLUSION Our developed nomogram model demonstrated good performance in predicting Ki-67 risk stratification and predicting survival outcomes in patients with HCC.
基金supported by the National Nat-ural Science Foundation of China(Nos.51872238,52074227,and 21806129)the Fundamental Research Funds for the Central Universities,China(Nos.3102018zy045 and 3102019AX11)+2 种基金the Guangdong Basic and Applied Basic Research Foundation,China(No.2024A1515010298)the Natural Science Basic Research Plan in Shaanxi Province of China(Nos.2017JQ5116 and 2020JM-118)the Key Laboratory of Icing and Anti/De-icing of CARDC(No.IADL20220401).
文摘Gels and conductive polymer composites,including hydrogen bonds(HBs),have emerged as promising materials for electro-magnetic wave(EMW)absorption across various applications.However,the relationship between conduction loss in EMW-absorbing materials and charge transfer in HB remains to be fully understood.In this study,we developed a series of deep eutectic gels to fine-tune the quantity of HB by adjusting the molar ratio of choline chloride(ChCl)and ethylene glycol(EG).Owing to the unique properties of deep eutectic gels,the effects of magnetic loss and polarization loss on EMW attenuation can be disregarded.Our results indicate that the quantity of HB initially increases and then decreases with the introduction of EG,with HB-induced conductive loss following similar pat-terns.At a ChCl and EG molar ratio of 2.4,the gel labeled G22-CE2.4 exhibited the best EMW absorption performance,characterized by an effective absorption bandwidth of 8.50 GHz and a thickness of 2.54 mm.This superior performance is attributed to the synergistic ef-fects of excellent conductive loss and impedance matching generated by the optimal number of HB.This work elucidates the role of HB in dielectric loss for the first time and provides valuable insights into the optimal design of supramolecular polymer absorbers.
基金financially supported by the National Natural Science Foundation of China(Grant Nos.U20A20266 and 12302503)Scientific and technological research projects in Sichuan province(Grant No.2024NSFSC0973).
文摘The loaded rock experiences multiple stages of deformation.It starts with the formation of microcracks at low stresses(crack initiation,CI)and then transitions into unstable crack propagation(crack damage,CD)near the ultimate strength.In this study,both the acoustic emission method(AEM)and the ultrasonic testing method(UTM)were used to examine the characteristics of AE parameters(b-value,peak frequency,frequency-band energy ratio,and fractal dimension)and ultrasonic(ULT)properties(velocity,amplitude,energy attenuation,and scattering attenuation)of bedded shale at CI,CD,and ultimate strength.The comparison involved analyzing the strain-based method(SBM),AEM,and UTM to determine the thresholds for damage stress.A fuzzy comprehensive evaluation model(FCEM)was created to describe the damage thresholds and hazard assessment.The results indicate that the optimal AE and ULT parameters for identifying CI and CD stress are ringing count,ultrasonic amplitude,energy attenuation,and scattering attenuation of the S-wave.Besides,damage thresholds were detected earlier by AE monitoring,ranging from 3 MPa to 10 MPa.CI and CD identified by UTM occurred later than SBM and AEM,and were in the range of 12 MPa.The b-value,peak frequency,energy ratio in the low-frequency band(0e62.5 kHz),correlation dimension,and sandbox dimension showed low values at the peak stress,while the energy ratio in a moderate-frequency band(187.5e281.25 kHz)and amplitude showed high values.The successful application of FCEM to laboratory testing of shales has demonstrated its ability to quantitatively identify AE/ULT precursors of seismic hazards associated with rock failure.
