Cultural artifacts exhibitions are among the most profound and appealing forms of cultural exchange.These exhibitions serve as an important bridge for fostering friendly relations between countries and bringing people...Cultural artifacts exhibitions are among the most profound and appealing forms of cultural exchange.These exhibitions serve as an important bridge for fostering friendly relations between countries and bringing people closer together.展开更多
This study aims to clarify the conceptual characteristics of artifact utilization in nursing practice instruction. Five selected articles were analyzed using the concept analysis method by Walker and Avant. The attrib...This study aims to clarify the conceptual characteristics of artifact utilization in nursing practice instruction. Five selected articles were analyzed using the concept analysis method by Walker and Avant. The attributes, antecedents, and consequences of the concept were extracted from the target literature. The analysis revealed two attributes (“connecting people to people” and “connecting people to objects”);two antecedents (“recognition of artifacts” and “selection of artifacts”);and two consequences (“designing a fulfilling learning environment” and “improving the quality of education”). The concept was defined as “promoting the utilization of artifacts by recognizing and selecting them, connecting people to people and people to objects, designing a fulfilling learning environment, and improving the quality of education”.展开更多
BACKGROUND Posterior lumbar interbody fusion has good clinical results,but adjacent segment disease(ASD)affects its long-term efficacy.In patients with L4-5 fusion who were followed up for more than 10 years,the ASD i...BACKGROUND Posterior lumbar interbody fusion has good clinical results,but adjacent segment disease(ASD)affects its long-term efficacy.In patients with L4-5 fusion who were followed up for more than 10 years,the ASD incidence was 33.3%.Magnetic resonance imaging(MRI)is key for ASD diagnosis,but metal artifacts from internal fixation limit its use;therefore,removing the artifacts is crucial for ASD diagnosis and treatment.AIM To evaluate the value of WARP MRI for patients with lumbar ASD.METHODS In our hospital,the lumbar spines of patients with ASD were assessed via lumbar MRI,including conventional sequences and sequences for artifacts.A PACS workstation was used for image measurement,analysis,and assessment,which mainly included measurement of the internal fixation implant artifact area,evaluation of the visibility of the anatomical structures surrounding the implant,and diagnostic assessment of ASD in the section.Conventional MRI data sequences and artifacts to sequence the contrast analysis of the MRI data.RESULTS A total of 30 patients with ASD after lumbar fusion and internal fixation were included in the study;the patients included 13 male and 17 female patients and were aged 66.03±5.83 years.The metal artifact area of the WARP T2-tirm sequence was significantly smaller than that of the conventional STIR sequence[(20.85±6.27)cm²vs(50.56±8.55)cm²,P<0.01].The WARP T2-tirm sequence was observed around the implants,pedicles,intervertebral foramen,and vertebral bodies,and the conventional STIR sequence clearly displayed nerve roots within the intervertebral foramen.In all 30 patients,all adjacent segments of the WARP T2-tirm sequence could be clearly observed(above Grade 4),whereas it was difficult to observe these segments in the conventional STIR sequence due to the presence of more severe metal artifacts.CONCLUSION WARP sequences can significantly reduce the artifact area in the sagittal and cross-sectional images of titanium alloy spinal fixation,providing a good imaging reference for the diagnosis of ASD.展开更多
Physiological signals such as electroencephalogram(EEG)signals are often corrupted by artifacts during the acquisition and processing.Some of these artifacts may deteriorate the essential properties of the signal that...Physiological signals such as electroencephalogram(EEG)signals are often corrupted by artifacts during the acquisition and processing.Some of these artifacts may deteriorate the essential properties of the signal that pertains to meaningful information.Most of these artifacts occur due to the involuntary movements or actions the human does during the acquisition process.So,it is recommended to eliminate these artifacts with signal processing approaches.This paper presents two mechanisms of classification and elimination of artifacts.In the first step,a customized deep network is employed to classify clean EEG signals and artifact-included signals.The classification is performed at the feature level,where common space pattern features are extracted with convolutional layers,and these features are later classified with a support vector machine classifier.In the second stage of the work,the artifact signals are decomposed with empirical mode decomposition,and they are then eliminated with the proposed adaptive thresholding mechanism where the threshold value changes for every intrinsic mode decomposition in the iterative mechanism.展开更多
The translation of ancient Chinese cultural artifacts terminology in museums plays a pivotal role in facilitating cross-cultural communication and preserving cultural heritage in the context of globalization.Through f...The translation of ancient Chinese cultural artifacts terminology in museums plays a pivotal role in facilitating cross-cultural communication and preserving cultural heritage in the context of globalization.Through field research in over ten Chinese museums and analysis of more than 800 terms,this paper explores the most effective translation methods for such terminology,focusing on transliteration and literal translation under the strategy of foreignization as well as liberal translation and imitation under the domesticating strategy.Standardized terminology not only prevents ambiguities but also bridges cultural gaps by preserving phonetic authenticity while contextualizing functionality.By refining translation practices,museums can better fulfill their mission as bridges between cultures,promoting global appreciation of China’s rich historical and artistic legacy.展开更多
It is not easy to reduce the metal artifacts of computed tomography images.However,the pixel values inside the metal artifact regions vary smoothly,while those on the borders of the metal and the bone regions vary sha...It is not easy to reduce the metal artifacts of computed tomography images.However,the pixel values inside the metal artifact regions vary smoothly,while those on the borders of the metal and the bone regions vary sharply.When the Canny operation by adaptive thresholding is conducted on the raw image,the almost continuous edges can be formed obviously on the borders of the metal and the bone regions,but this kind of information cannot be formed for the metal artifact regions.In this paper,by searching the closed areas formed by the border edges of the bone regions in the Canny image,the metal artifact regions,which are very difficult to discriminate only by intensity thresholding,can be excluded effectively.A novel prior image-based method is thus developed for metal artifact reduction.The experiments demonstrate that the proposed method can be realized easily and reduce the metal artifacts effectively even if multiple large metal objects exist simultaneously in the image.The method is suitable for the clinical application.展开更多
Removing different types of artifacts from the electroencephalography(EEG)recordings is a critical step in performing EEG signal analysis and diagnosis.Most of the existing algorithms aim for removing single type of a...Removing different types of artifacts from the electroencephalography(EEG)recordings is a critical step in performing EEG signal analysis and diagnosis.Most of the existing algorithms aim for removing single type of artifacts,leading to a complex system if an EEG recording contains different types of artifacts.With the advancement in wearable technologies,it is necessary to develop an energy-efficient algorithm to deal with different types of artifacts for single-channel wearable EEG devices.In this paper,an automatic EEG artifact removal algorithm is proposed that effectively reduces three types of artifacts,i.e.,ocular artifact(OA),transmission-line/harmonic-wave artifact(TA/HA),and muscle artifact(MA),from a single-channel EEG recording.The effectiveness of the proposed algorithm is verified on both simulated noisy EEG signals and real EEG from CHB-MIT dataset.The experimental results show that the proposed algorithm effectively suppresses OA,MA and TA/HA from a single-channel EEG recording as well as physical movement artifact.展开更多
Limited by the dynamic range of the detector,saturation artifacts usually occur in optical coherence tomography(OCT)imaging for high scattering media.The available methods are difficult to remove saturation artifacts ...Limited by the dynamic range of the detector,saturation artifacts usually occur in optical coherence tomography(OCT)imaging for high scattering media.The available methods are difficult to remove saturation artifacts and restore texture completely in OCT images.We proposed a deep learning-based inpainting method of saturation artifacts in this paper.The generation mechanism of saturation artifacts was analyzed,and experimental and simulated datasets were built based on the mechanism.Enhanced super-resolution generative adversarial networks were trained by the clear–saturated phantom image pairs.The perfect reconstructed results of experimental zebrafish and thyroid OCT images proved its feasibility,strong generalization,and robustness.展开更多
Optical coherence tomography(OCT)imaging technology has significant advantages in in situ and noninvasive monitoring of biological tissues.However,it still faces the following challenges:including data processing spee...Optical coherence tomography(OCT)imaging technology has significant advantages in in situ and noninvasive monitoring of biological tissues.However,it still faces the following challenges:including data processing speed,image quality,and improvements in three-dimensional(3D)visualization effects.OCT technology,especially functional imaging techniques like optical coherence tomography angiography(OCTA),requires a long acquisition time and a large data size.Despite the substantial increase in the acquisition speed of swept source optical coherence tomography(SS-OCT),it still poses significant challenges for data processing.Additionally,during in situ acquisition,image artifacts resulting from interface reflections or strong reflections from biological tissues and culturing containers present obstacles to data visualization and further analysis.Firstly,a customized frequency domainfilter with anti-banding suppression parameters was designed to suppress artifact noises.Then,this study proposed a graphics processing unit(GPU)-based real-time data processing pipeline for SS-OCT,achieving a measured line-process rate of 800 kHz for 3D fast and high-quality data visualization.Furthermore,a GPU-based realtime data processing for CC-OCTA was integrated to acquire dynamic information.Moreover,a vascular-like network chip was prepared using extrusion-based 3D printing and sacrificial materials,with sacrificial material being printed at the desired vascular network locations and then removed to form the vascular-like network.OCTA imaging technology was used to monitor the progression of sacrificial material removal and vascular-like network formation.Therefore,GPU-based OCT enables real-time processing and visualization with artifact suppression,making it particularly suitable for in situ noninvasive longitudinal monitoring of 3D bioprinting tissue and vascular-like networks in microfluidic chips.展开更多
This study addresses challenges in fetal magnetic resonance imaging (MRI) related to motion artifacts, maternal respiration, and hardware limitations. To enhance MRI quality, we employ deep learning techniques, specif...This study addresses challenges in fetal magnetic resonance imaging (MRI) related to motion artifacts, maternal respiration, and hardware limitations. To enhance MRI quality, we employ deep learning techniques, specifically utilizing Cycle GAN. Synthetic pairs of images, simulating artifacts in fetal MRI, are generated to train the model. Our primary contribution is the use of Cycle GAN for fetal MRI restoration, augmented by artificially corrupted data. We compare three approaches (supervised Cycle GAN, Pix2Pix, and Mobile Unet) for artifact removal. Experimental results demonstrate that the proposed supervised Cycle GAN effectively removes artifacts while preserving image details, as validated through Structural Similarity Index Measure (SSIM) and normalized Mean Absolute Error (MAE). The method proves comparable to alternatives but avoids the generation of spurious regions, which is crucial for medical accuracy.展开更多
Digital forensics aims to uncover evidence of cybercrimes within compromised systems.These cybercrimes are often perpetrated through the deployment of malware,which inevitably leaves discernible traces within the comp...Digital forensics aims to uncover evidence of cybercrimes within compromised systems.These cybercrimes are often perpetrated through the deployment of malware,which inevitably leaves discernible traces within the compromised systems.Forensic analysts are tasked with extracting and subsequently analyzing data,termed as artifacts,from these systems to gather evidence.Therefore,forensic analysts must sift through extensive datasets to isolate pertinent evidence.However,manually identifying suspicious traces among numerous artifacts is time-consuming and labor-intensive.Previous studies addressed such inefficiencies by integrating artificial intelligence(AI)technologies into digital forensics.Despite the efforts in previous studies,artifacts were analyzed without considering the nature of the data within them and failed to prove their efficiency through specific evaluations.In this study,we propose a system to prioritize suspicious artifacts from compromised systems infected with malware to facilitate efficient digital forensics.Our system introduces a double-checking method that recognizes the nature of data within target artifacts and employs algorithms ideal for anomaly detection.The key ideas of this method are:(1)prioritize suspicious artifacts and filter remaining artifacts using autoencoder and(2)further prioritize suspicious artifacts and filter remaining artifacts using logarithmic entropy.Our evaluation demonstrates that our system can identify malicious artifacts with high accuracy and that its double-checking method is more efficient than alternative approaches.Our system can significantly reduce the time required for forensic analysis and serve as a reference for future studies.展开更多
The importance of the accuracy of preparing biological specimen as histological sections that can be examined under a microscope lies in reflecting a true image of the tissue that includes all its components, which ar...The importance of the accuracy of preparing biological specimen as histological sections that can be examined under a microscope lies in reflecting a true image of the tissue that includes all its components, which are used in scientific research or for the purpose of diagnosing various diseases of the body. Despite this, some cellular structures within the tissue may suffer from some alterations that result from the appearance of defects during any stage of preparing these microscopic sections, which alter or interfere with the precise cellular structures and morphology that constitute the tissue and thus give a different image for tissue features and cause confusion in the work histopathologist in the diagnosis. There are several reasons that can cause a misdiagnosis of the sample that occurs during the surgical separation process or after separation during the stages of microscopic preparation techniques from fixation stage, tissue processing, embedding or microtomy, staining until mounting procedures. The constant need to identify these defects and their causes in addition to try to reduce them is one of the biggest challenges evident in pathology laboratories. Therefore, this study aims to review the most common defects that occur in any stage of tissue processing, with an explanation of their causes and appropriate ways to avoid them.展开更多
This study presents an energy consumption(EC)forecasting method for laser melting manufacturing of metal artifacts based on fusionable transfer learning(FTL).To predict the EC of manufacturing products,particularly fr...This study presents an energy consumption(EC)forecasting method for laser melting manufacturing of metal artifacts based on fusionable transfer learning(FTL).To predict the EC of manufacturing products,particularly from scale-down to scale-up,a general paradigm was first developed by categorizing the overall process into three main sub-steps.The operating electrical power was further formulated as a combinatorial function,based on which an operator learning network was adopted to fit the nonlinear relations between the fabricating arguments and EC.Parallel-arranged networks were constructed to investigate the impacts of fabrication variables and devices on power.Considering the interconnections among these factors,the outputs of the neural networks were blended and fused to jointly predict the electrical power.Most innovatively,large artifacts can be decomposed into timedependent laser-scanning trajectories,which can be further transformed into fusionable information via neural networks,inspired by large language model.Accordingly,transfer learning can deal with either scale-down or scale-up forecasting,namely,FTL with scalability within artifact structures.The effectiveness of the proposed FTL was verified through physical fabrication experiments via laser powder bed fusion.The relative error of the average and overall EC predictions based on FTL was maintained below 0.83%.The melting fusion quality was examined using metallographic diagrams.The proposed FTL framework can forecast the EC of scaled structures,which is particularly helpful in price estimation and quotation of large metal products towards carbon peaking and carbon neutrality.展开更多
文摘Cultural artifacts exhibitions are among the most profound and appealing forms of cultural exchange.These exhibitions serve as an important bridge for fostering friendly relations between countries and bringing people closer together.
文摘This study aims to clarify the conceptual characteristics of artifact utilization in nursing practice instruction. Five selected articles were analyzed using the concept analysis method by Walker and Avant. The attributes, antecedents, and consequences of the concept were extracted from the target literature. The analysis revealed two attributes (“connecting people to people” and “connecting people to objects”);two antecedents (“recognition of artifacts” and “selection of artifacts”);and two consequences (“designing a fulfilling learning environment” and “improving the quality of education”). The concept was defined as “promoting the utilization of artifacts by recognizing and selecting them, connecting people to people and people to objects, designing a fulfilling learning environment, and improving the quality of education”.
基金Supported by Shanghai Tongren Hospital Scientific Research Funds,No.TRKYRC-xx202203Shanghai Municipal Health Commission,No.2022YQ006Science and Technology Commission of Shanghai Municipality,No.22ZR1457200.
文摘BACKGROUND Posterior lumbar interbody fusion has good clinical results,but adjacent segment disease(ASD)affects its long-term efficacy.In patients with L4-5 fusion who were followed up for more than 10 years,the ASD incidence was 33.3%.Magnetic resonance imaging(MRI)is key for ASD diagnosis,but metal artifacts from internal fixation limit its use;therefore,removing the artifacts is crucial for ASD diagnosis and treatment.AIM To evaluate the value of WARP MRI for patients with lumbar ASD.METHODS In our hospital,the lumbar spines of patients with ASD were assessed via lumbar MRI,including conventional sequences and sequences for artifacts.A PACS workstation was used for image measurement,analysis,and assessment,which mainly included measurement of the internal fixation implant artifact area,evaluation of the visibility of the anatomical structures surrounding the implant,and diagnostic assessment of ASD in the section.Conventional MRI data sequences and artifacts to sequence the contrast analysis of the MRI data.RESULTS A total of 30 patients with ASD after lumbar fusion and internal fixation were included in the study;the patients included 13 male and 17 female patients and were aged 66.03±5.83 years.The metal artifact area of the WARP T2-tirm sequence was significantly smaller than that of the conventional STIR sequence[(20.85±6.27)cm²vs(50.56±8.55)cm²,P<0.01].The WARP T2-tirm sequence was observed around the implants,pedicles,intervertebral foramen,and vertebral bodies,and the conventional STIR sequence clearly displayed nerve roots within the intervertebral foramen.In all 30 patients,all adjacent segments of the WARP T2-tirm sequence could be clearly observed(above Grade 4),whereas it was difficult to observe these segments in the conventional STIR sequence due to the presence of more severe metal artifacts.CONCLUSION WARP sequences can significantly reduce the artifact area in the sagittal and cross-sectional images of titanium alloy spinal fixation,providing a good imaging reference for the diagnosis of ASD.
文摘Physiological signals such as electroencephalogram(EEG)signals are often corrupted by artifacts during the acquisition and processing.Some of these artifacts may deteriorate the essential properties of the signal that pertains to meaningful information.Most of these artifacts occur due to the involuntary movements or actions the human does during the acquisition process.So,it is recommended to eliminate these artifacts with signal processing approaches.This paper presents two mechanisms of classification and elimination of artifacts.In the first step,a customized deep network is employed to classify clean EEG signals and artifact-included signals.The classification is performed at the feature level,where common space pattern features are extracted with convolutional layers,and these features are later classified with a support vector machine classifier.In the second stage of the work,the artifact signals are decomposed with empirical mode decomposition,and they are then eliminated with the proposed adaptive thresholding mechanism where the threshold value changes for every intrinsic mode decomposition in the iterative mechanism.
文摘The translation of ancient Chinese cultural artifacts terminology in museums plays a pivotal role in facilitating cross-cultural communication and preserving cultural heritage in the context of globalization.Through field research in over ten Chinese museums and analysis of more than 800 terms,this paper explores the most effective translation methods for such terminology,focusing on transliteration and literal translation under the strategy of foreignization as well as liberal translation and imitation under the domesticating strategy.Standardized terminology not only prevents ambiguities but also bridges cultural gaps by preserving phonetic authenticity while contextualizing functionality.By refining translation practices,museums can better fulfill their mission as bridges between cultures,promoting global appreciation of China’s rich historical and artistic legacy.
文摘It is not easy to reduce the metal artifacts of computed tomography images.However,the pixel values inside the metal artifact regions vary smoothly,while those on the borders of the metal and the bone regions vary sharply.When the Canny operation by adaptive thresholding is conducted on the raw image,the almost continuous edges can be formed obviously on the borders of the metal and the bone regions,but this kind of information cannot be formed for the metal artifact regions.In this paper,by searching the closed areas formed by the border edges of the bone regions in the Canny image,the metal artifact regions,which are very difficult to discriminate only by intensity thresholding,can be excluded effectively.A novel prior image-based method is thus developed for metal artifact reduction.The experiments demonstrate that the proposed method can be realized easily and reduce the metal artifacts effectively even if multiple large metal objects exist simultaneously in the image.The method is suitable for the clinical application.
基金the National Natural Science Foundation of China(No.61874171)the Alibaba Innovative Research Program of Alibaba Group。
文摘Removing different types of artifacts from the electroencephalography(EEG)recordings is a critical step in performing EEG signal analysis and diagnosis.Most of the existing algorithms aim for removing single type of artifacts,leading to a complex system if an EEG recording contains different types of artifacts.With the advancement in wearable technologies,it is necessary to develop an energy-efficient algorithm to deal with different types of artifacts for single-channel wearable EEG devices.In this paper,an automatic EEG artifact removal algorithm is proposed that effectively reduces three types of artifacts,i.e.,ocular artifact(OA),transmission-line/harmonic-wave artifact(TA/HA),and muscle artifact(MA),from a single-channel EEG recording.The effectiveness of the proposed algorithm is verified on both simulated noisy EEG signals and real EEG from CHB-MIT dataset.The experimental results show that the proposed algorithm effectively suppresses OA,MA and TA/HA from a single-channel EEG recording as well as physical movement artifact.
基金supported by the National Natural Science Foundation of China(62375144 and 61875092)Tianjin Foundation of Natural Science(21JCYBJC00260)Beijing-Tianjin-Hebei Basic Research Cooperation Special Program(19JCZDJC65300).
文摘Limited by the dynamic range of the detector,saturation artifacts usually occur in optical coherence tomography(OCT)imaging for high scattering media.The available methods are difficult to remove saturation artifacts and restore texture completely in OCT images.We proposed a deep learning-based inpainting method of saturation artifacts in this paper.The generation mechanism of saturation artifacts was analyzed,and experimental and simulated datasets were built based on the mechanism.Enhanced super-resolution generative adversarial networks were trained by the clear–saturated phantom image pairs.The perfect reconstructed results of experimental zebrafish and thyroid OCT images proved its feasibility,strong generalization,and robustness.
基金supported by the National Key Research and Development Program of China(Nos.2022YFA1104600 and 2022YFA1200208)National Natural Science Foundation of China(No.31927801)Key Research and Development Foundation of Zhejiang Province(No.2022C01123).
文摘Optical coherence tomography(OCT)imaging technology has significant advantages in in situ and noninvasive monitoring of biological tissues.However,it still faces the following challenges:including data processing speed,image quality,and improvements in three-dimensional(3D)visualization effects.OCT technology,especially functional imaging techniques like optical coherence tomography angiography(OCTA),requires a long acquisition time and a large data size.Despite the substantial increase in the acquisition speed of swept source optical coherence tomography(SS-OCT),it still poses significant challenges for data processing.Additionally,during in situ acquisition,image artifacts resulting from interface reflections or strong reflections from biological tissues and culturing containers present obstacles to data visualization and further analysis.Firstly,a customized frequency domainfilter with anti-banding suppression parameters was designed to suppress artifact noises.Then,this study proposed a graphics processing unit(GPU)-based real-time data processing pipeline for SS-OCT,achieving a measured line-process rate of 800 kHz for 3D fast and high-quality data visualization.Furthermore,a GPU-based realtime data processing for CC-OCTA was integrated to acquire dynamic information.Moreover,a vascular-like network chip was prepared using extrusion-based 3D printing and sacrificial materials,with sacrificial material being printed at the desired vascular network locations and then removed to form the vascular-like network.OCTA imaging technology was used to monitor the progression of sacrificial material removal and vascular-like network formation.Therefore,GPU-based OCT enables real-time processing and visualization with artifact suppression,making it particularly suitable for in situ noninvasive longitudinal monitoring of 3D bioprinting tissue and vascular-like networks in microfluidic chips.
文摘This study addresses challenges in fetal magnetic resonance imaging (MRI) related to motion artifacts, maternal respiration, and hardware limitations. To enhance MRI quality, we employ deep learning techniques, specifically utilizing Cycle GAN. Synthetic pairs of images, simulating artifacts in fetal MRI, are generated to train the model. Our primary contribution is the use of Cycle GAN for fetal MRI restoration, augmented by artificially corrupted data. We compare three approaches (supervised Cycle GAN, Pix2Pix, and Mobile Unet) for artifact removal. Experimental results demonstrate that the proposed supervised Cycle GAN effectively removes artifacts while preserving image details, as validated through Structural Similarity Index Measure (SSIM) and normalized Mean Absolute Error (MAE). The method proves comparable to alternatives but avoids the generation of spurious regions, which is crucial for medical accuracy.
基金supported by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2024-RS-2024-00437494)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation).
文摘Digital forensics aims to uncover evidence of cybercrimes within compromised systems.These cybercrimes are often perpetrated through the deployment of malware,which inevitably leaves discernible traces within the compromised systems.Forensic analysts are tasked with extracting and subsequently analyzing data,termed as artifacts,from these systems to gather evidence.Therefore,forensic analysts must sift through extensive datasets to isolate pertinent evidence.However,manually identifying suspicious traces among numerous artifacts is time-consuming and labor-intensive.Previous studies addressed such inefficiencies by integrating artificial intelligence(AI)technologies into digital forensics.Despite the efforts in previous studies,artifacts were analyzed without considering the nature of the data within them and failed to prove their efficiency through specific evaluations.In this study,we propose a system to prioritize suspicious artifacts from compromised systems infected with malware to facilitate efficient digital forensics.Our system introduces a double-checking method that recognizes the nature of data within target artifacts and employs algorithms ideal for anomaly detection.The key ideas of this method are:(1)prioritize suspicious artifacts and filter remaining artifacts using autoencoder and(2)further prioritize suspicious artifacts and filter remaining artifacts using logarithmic entropy.Our evaluation demonstrates that our system can identify malicious artifacts with high accuracy and that its double-checking method is more efficient than alternative approaches.Our system can significantly reduce the time required for forensic analysis and serve as a reference for future studies.
文摘The importance of the accuracy of preparing biological specimen as histological sections that can be examined under a microscope lies in reflecting a true image of the tissue that includes all its components, which are used in scientific research or for the purpose of diagnosing various diseases of the body. Despite this, some cellular structures within the tissue may suffer from some alterations that result from the appearance of defects during any stage of preparing these microscopic sections, which alter or interfere with the precise cellular structures and morphology that constitute the tissue and thus give a different image for tissue features and cause confusion in the work histopathologist in the diagnosis. There are several reasons that can cause a misdiagnosis of the sample that occurs during the surgical separation process or after separation during the stages of microscopic preparation techniques from fixation stage, tissue processing, embedding or microtomy, staining until mounting procedures. The constant need to identify these defects and their causes in addition to try to reduce them is one of the biggest challenges evident in pathology laboratories. Therefore, this study aims to review the most common defects that occur in any stage of tissue processing, with an explanation of their causes and appropriate ways to avoid them.
基金funded by the National Key Research and Development Program of China,No.2022YFB3303303Key Open Fund of State Key Lab of Materials Processing and Die&Mould Technology of China,No.P2024-001Zhejiang Provincial Research and Development Project of China,No.LGG22E050010。
文摘This study presents an energy consumption(EC)forecasting method for laser melting manufacturing of metal artifacts based on fusionable transfer learning(FTL).To predict the EC of manufacturing products,particularly from scale-down to scale-up,a general paradigm was first developed by categorizing the overall process into three main sub-steps.The operating electrical power was further formulated as a combinatorial function,based on which an operator learning network was adopted to fit the nonlinear relations between the fabricating arguments and EC.Parallel-arranged networks were constructed to investigate the impacts of fabrication variables and devices on power.Considering the interconnections among these factors,the outputs of the neural networks were blended and fused to jointly predict the electrical power.Most innovatively,large artifacts can be decomposed into timedependent laser-scanning trajectories,which can be further transformed into fusionable information via neural networks,inspired by large language model.Accordingly,transfer learning can deal with either scale-down or scale-up forecasting,namely,FTL with scalability within artifact structures.The effectiveness of the proposed FTL was verified through physical fabrication experiments via laser powder bed fusion.The relative error of the average and overall EC predictions based on FTL was maintained below 0.83%.The melting fusion quality was examined using metallographic diagrams.The proposed FTL framework can forecast the EC of scaled structures,which is particularly helpful in price estimation and quotation of large metal products towards carbon peaking and carbon neutrality.
