The concept of emissivity has been with the scientific and engineering world since Planck formulated his blackbody radiation law more than a century ago.Nevertheless,emissivity is an elusive concept even for ex⁃perts....The concept of emissivity has been with the scientific and engineering world since Planck formulated his blackbody radiation law more than a century ago.Nevertheless,emissivity is an elusive concept even for ex⁃perts.It is a vague and fuzzy concept for the wider community of engineers.The importance of remote sensing of temperature by measuring IR radiation has been recognized in a wide range of industrial,medical,and environ⁃mental uses.One of the major sources of errors in IR radiometry is the emissivity of the surface being measured.In real experiments,emissivity may be influenced by many factors:surface texture,spectral properties,oxida⁃tion,and aging of surfaces.While commercial blackbodies are prevalent,the much-needed grey bodies with a known emissivity,are unavailable.This study describes how to achieve a calibrated and stable emissivity with a blackbody,a perforated screen,and a reliable and linear novel IR thermal sensor,18 dubbed TMOS.The Digital TMOS is now a low-cost commercial product,it requires low power,and it has a small form factor.The method⁃ology is based on two-color measurements,with two different optical filters,with selected wavelengths conform⁃ing to the grey body definition of the use case under study.With a photochemically etched perforated screen,the effective emissivity of the screen is simply the hole density area of the surface area that emits according to the blackbody temperature radiation.The concept is illustrated with ray tracing simulations,which demonstrate the approach.Measured results are reported.展开更多
Objective To establish a body composition analysis system based on chest CT,and to observe its value for evaluating content of chest muscle and adipose.Methods T7—T8 layer CT images of 108 pneumonia patients were col...Objective To establish a body composition analysis system based on chest CT,and to observe its value for evaluating content of chest muscle and adipose.Methods T7—T8 layer CT images of 108 pneumonia patients were collected(segmented dataset),and chest CT data of 984 patients were screened from the COVID 19-CT dataset(10 cases were randomly selected as whole test dataset,the remaining 974 cases were selected as layer selection dataset).T7—T8 layer was classified based on convolutional neural network(CNN)derived networks,including ResNet,ResNeXt,MobileNet,ShuffleNet,DenseNet,EfficientNet and ConvNeXt,then the accuracy,precision,recall and specificity were used to evaluate the performance of layer selection dataset.The skeletal muscle(SM),subcutaneous adipose tissue(SAT),intermuscular adipose tissue(IMAT)and visceral adipose tissue(VAT)were segmented using classical fully CNN(FCN)derived network,including FCN,SegNet,UNet,Attention UNet,UNET++,nnUNet,UNeXt and CMUNeXt,then Dice similarity coefficient(DSC),intersection over union(IoU)and 95 Hausdorff distance(HD)were used to evaluate the performance of segmented dataset.The automatic body composition analysis system was constructed based on optimal layer selection network and segmentation network,the mean absolute error(MAE),root mean squared error(RMSE)and standard deviation(SD)of MAE were used to evaluate the performance of automatic system for testing the whole test dataset.Results The accuracy,precision,recall and specificity of DenseNet network for automatically classifying T7—T8 layer from chest CT images was 95.06%,84.83%,92.27%and 95.78%,respectively,which were all higher than those of the other layer selection networks.In segmentation of SM,SAT,IMAT and overall,DSC and IoU of UNet++network were all higher,while 95HD of UNet++network were all lower than those of the other segmentation networks.Using DenseNet as the layer selection network and UNet++as the segmentation network,MAE of the automatic body composition analysis system for predicting SM,SAT,IMAT,VAT and MAE was 27.09,6.95,6.65 and 3.35 cm 2,respectively.Conclusion The body composition analysis system based on chest CT could be used to assess content of chest muscle and adipose.Among them,the UNet++network had better segmentation performance in adipose tissue than SM.展开更多
Exosomes,ubiquitously present in body fluids,serve as non-invasive biomarkers for disease diagnosis,monitoring,and treatment.As intercellular messengers,exosomes encapsulate a rich array of proteins,nucleic acids,and ...Exosomes,ubiquitously present in body fluids,serve as non-invasive biomarkers for disease diagnosis,monitoring,and treatment.As intercellular messengers,exosomes encapsulate a rich array of proteins,nucleic acids,and metabolites,although most studies have primarily focused on proteins and RNA.Recently,exosome metabolomics has demonstrated clinical value and potential advantages in disease detection and pathophysiology,despite significant challenges,particularly in exosome isolation and metabolite detection.This review discusses the significant technical challenges in exosome isolation and metabolite detection,highlighting the advancements in these areas that support the clinical application of exosome metabolomics,and illustrates the potential of exosomal metabolites from various body fluids as biomarkers for early disease diagnosis and treatment.展开更多
Fatigue and tensile behaviors of homogenized WE 54 magnesium alloy before and after immersion in simulated body fluid(SBF)were investigated.According to the tensile test,the alloy without immersion in SBF solution has...Fatigue and tensile behaviors of homogenized WE 54 magnesium alloy before and after immersion in simulated body fluid(SBF)were investigated.According to the tensile test,the alloy without immersion in SBF solution has the highest tensile strength of 278 MPa,which decreased to 190 MPa after 336 h of immersion..The fatigue life of the homogenized WE 54 magnesium alloy before immersion in the SBF solution under a constant stress of 15 MPa is 3598 cycles.However,the fatigue life of the alloy decreased to 453 cycles after 336 h of immersion in the SBF solution under the same stress.Examination of the fracture surface of the samples by SEM reveals that the origin of the fatigue crack before immersion is micro-pores and defects.While corrosion pits and cracks are the main reasons for forming the initial fatigue crack after immersion.Moreover,the results obtained from practical work were evaluated and compared to theoretical calculations.The area of the hysteresis loops of the samples after the fatigue test,determined using Triangles and Monte Carlo methods,decreased from 4954.5 MPa and 4842.9 MPa before immersion to 192.0 MPa and 175.8 MPa after 336 h of immersion,respectively.展开更多
文摘The concept of emissivity has been with the scientific and engineering world since Planck formulated his blackbody radiation law more than a century ago.Nevertheless,emissivity is an elusive concept even for ex⁃perts.It is a vague and fuzzy concept for the wider community of engineers.The importance of remote sensing of temperature by measuring IR radiation has been recognized in a wide range of industrial,medical,and environ⁃mental uses.One of the major sources of errors in IR radiometry is the emissivity of the surface being measured.In real experiments,emissivity may be influenced by many factors:surface texture,spectral properties,oxida⁃tion,and aging of surfaces.While commercial blackbodies are prevalent,the much-needed grey bodies with a known emissivity,are unavailable.This study describes how to achieve a calibrated and stable emissivity with a blackbody,a perforated screen,and a reliable and linear novel IR thermal sensor,18 dubbed TMOS.The Digital TMOS is now a low-cost commercial product,it requires low power,and it has a small form factor.The method⁃ology is based on two-color measurements,with two different optical filters,with selected wavelengths conform⁃ing to the grey body definition of the use case under study.With a photochemically etched perforated screen,the effective emissivity of the screen is simply the hole density area of the surface area that emits according to the blackbody temperature radiation.The concept is illustrated with ray tracing simulations,which demonstrate the approach.Measured results are reported.
文摘Objective To establish a body composition analysis system based on chest CT,and to observe its value for evaluating content of chest muscle and adipose.Methods T7—T8 layer CT images of 108 pneumonia patients were collected(segmented dataset),and chest CT data of 984 patients were screened from the COVID 19-CT dataset(10 cases were randomly selected as whole test dataset,the remaining 974 cases were selected as layer selection dataset).T7—T8 layer was classified based on convolutional neural network(CNN)derived networks,including ResNet,ResNeXt,MobileNet,ShuffleNet,DenseNet,EfficientNet and ConvNeXt,then the accuracy,precision,recall and specificity were used to evaluate the performance of layer selection dataset.The skeletal muscle(SM),subcutaneous adipose tissue(SAT),intermuscular adipose tissue(IMAT)and visceral adipose tissue(VAT)were segmented using classical fully CNN(FCN)derived network,including FCN,SegNet,UNet,Attention UNet,UNET++,nnUNet,UNeXt and CMUNeXt,then Dice similarity coefficient(DSC),intersection over union(IoU)and 95 Hausdorff distance(HD)were used to evaluate the performance of segmented dataset.The automatic body composition analysis system was constructed based on optimal layer selection network and segmentation network,the mean absolute error(MAE),root mean squared error(RMSE)and standard deviation(SD)of MAE were used to evaluate the performance of automatic system for testing the whole test dataset.Results The accuracy,precision,recall and specificity of DenseNet network for automatically classifying T7—T8 layer from chest CT images was 95.06%,84.83%,92.27%and 95.78%,respectively,which were all higher than those of the other layer selection networks.In segmentation of SM,SAT,IMAT and overall,DSC and IoU of UNet++network were all higher,while 95HD of UNet++network were all lower than those of the other segmentation networks.Using DenseNet as the layer selection network and UNet++as the segmentation network,MAE of the automatic body composition analysis system for predicting SM,SAT,IMAT,VAT and MAE was 27.09,6.95,6.65 and 3.35 cm 2,respectively.Conclusion The body composition analysis system based on chest CT could be used to assess content of chest muscle and adipose.Among them,the UNet++network had better segmentation performance in adipose tissue than SM.
文摘Exosomes,ubiquitously present in body fluids,serve as non-invasive biomarkers for disease diagnosis,monitoring,and treatment.As intercellular messengers,exosomes encapsulate a rich array of proteins,nucleic acids,and metabolites,although most studies have primarily focused on proteins and RNA.Recently,exosome metabolomics has demonstrated clinical value and potential advantages in disease detection and pathophysiology,despite significant challenges,particularly in exosome isolation and metabolite detection.This review discusses the significant technical challenges in exosome isolation and metabolite detection,highlighting the advancements in these areas that support the clinical application of exosome metabolomics,and illustrates the potential of exosomal metabolites from various body fluids as biomarkers for early disease diagnosis and treatment.
文摘Fatigue and tensile behaviors of homogenized WE 54 magnesium alloy before and after immersion in simulated body fluid(SBF)were investigated.According to the tensile test,the alloy without immersion in SBF solution has the highest tensile strength of 278 MPa,which decreased to 190 MPa after 336 h of immersion..The fatigue life of the homogenized WE 54 magnesium alloy before immersion in the SBF solution under a constant stress of 15 MPa is 3598 cycles.However,the fatigue life of the alloy decreased to 453 cycles after 336 h of immersion in the SBF solution under the same stress.Examination of the fracture surface of the samples by SEM reveals that the origin of the fatigue crack before immersion is micro-pores and defects.While corrosion pits and cracks are the main reasons for forming the initial fatigue crack after immersion.Moreover,the results obtained from practical work were evaluated and compared to theoretical calculations.The area of the hysteresis loops of the samples after the fatigue test,determined using Triangles and Monte Carlo methods,decreased from 4954.5 MPa and 4842.9 MPa before immersion to 192.0 MPa and 175.8 MPa after 336 h of immersion,respectively.
