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IEEE Instrumentation and Measurement technology Conference

作者： Katelyn R. Brinker Devdatt Chattopadhyay Logan M. Wilcox Kristen M. Donnell Department of Electrical and Computer Engineering (ECpE) Iowa State University Ames IA USA Department of Electrical and Computer Engineering (ECE) Missouri University of Science and Technology Rolla MO USA

ISBN: (数字)9781728144603

ISBN: (纸本)9781728144610

Biodegradable glass is a special category of glass that is widely used in biomedical engineering for the repair and regeneration of soft and hard tissues and other rehabilitation purposes. This type of glass degrades when implanted inside the human body and gets absorbed by the body without producing any harmful effects. Hence, these types of glasses can be potentially used as a substrate for fabricating implantable and temporary biomedical sensors. However, in order to be able to design and simulate these sensors, it is critical that the dielectric properties of these biodegradable glass substrates are known. To this end, the dielectric properties of anhydrous sodium tetra borate biodegradable glass are characterized along with those of a commercial glass slide using the open-ended waveguide method. In addition, the fabricated biodegradable glass substrates and the commercial slide glass were tested for residual stress development and surface roughness, which plays an important role in determining the accuracy of the measured data.

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Thermal behavior of materials in laser-assisted extreme manufacturing:Raman-based novel characterization

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International Journal of Extreme Manufacturing 2020年第3期2卷 86-102页

作者： Ridong Wang Shen Xu Yanan Yue Xinwei Wang State Key Laboratory of Precision Measuring Technology and Instruments Tianjin UniversityTianjin 300072People's Republic of China School of Mechanical and Automotive Engineering Shanghai University of Engineering ScienceShanghai 201620People's Republic of China School of Power and Mechanical Engineering Wuhan UniversityWuhan 430072People's Republic of China Department of Mechanical Engineering Iowa State UniversityAmesIA 50011United States of America

Laser-assisted manufacturing(LAM)is a technique that performs machining of materials using a laser heating *** the process,temperatures can rise above over 2000°*** a result,it is crucial to explore the thermal behavior of materials under such high temperatures to understand the physics behind LAM and provide feedback for manufacturing *** spectroscopy,which is widely used for structure characterization,can provide a novel way to measure temperature during *** this review,we discuss the mechanism of Raman-based temperature probing,its calibration,and sources of uncertainty/error,and how to control *** critically review the Raman-based temperature measurement considering the spatial resolution under near-field optical heating and surface structure-induced *** another critical aspect of Raman-based temperature measurement,temporal resolution is also reviewed to cover various ways of realizing ultrafast thermal *** conclude with a detailed outlook on Raman-based temperature probing in LAM and issues that need special attention.

关键词： Raman spectroscopy temperature response spatial resolution temporal resolution ultrafast characterization

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Re-understanding of land surface albedo and related terms in satellite-based retrievals

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Big Earth Data 2020年第1期4卷 45-67页

作者： Yanmin Shuai Latipa Tuerhanjiang Congying Shao Feng Gao Yuyu Zhou Donghui Xie Tao Liu Ji Liang Nan Chu School of Geomatics and Geography Liaoning Technical UniversityFuxinLiaoningChina State Key Laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and Geographyand Research Center for Ecology and Environment of Central AsiaCASUrumqiChina School for the Environment University of MassachusettsBostonMAUSA College of Resource and Environment Sciences Xinjiang UniversityXinjiangChina USDA-ARS Hydrology and Remote Sensing Laboratory BeltsvilleMDUSA Department of Geological and Atmospheric Sciences Iowa State UniversityAmesIAUSA School of Geography and Remote Sensing Science Beijing Normal UniversityBeijingChina National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology Hunan University of Science and TechnologyXiangtanHunanChina

Land surface albedo is a critical variable in determining surface energy balance,and regulating climate and ecosystem processes through feedback ***,climatic modelers and radiative monitoring require accurate estimates of land surface *** the instrument development,algorithm upgrade,spectral-band-adjustment in wavelength center or band width,and the increasing distinct requirement from diversified communities,various albedo terms have been generated in related satellite-based *** lack of understanding on the divergence of these terminologies can introduce potential considerable errors in the subsequent applications,or an elevated probability to invert the deduced *** surveyed the basic concepts of reflectance quantities,retrieval strategies,and models developed since the 1970s,and discuss both strength and opportunity for improvements on land surface albedo extraction,and product *** addition,we exemplified the difference of albedo terms using the daily MODIS product(MCD43A)to emphasize the potential risk of the ambiguous usage,over typical IGBP land covers in Northern *** investigation shows that relative differences among various albedo terms can reach up to 181%and 50%,while 0.266 and 0.118 of absolute variance respectively in the narrow and broad-band surface albedo,which illuminated cautions against the ambiguous understanding of albedo terminologies or erroneous usage of albedo products.

关键词： Surface albedo BRDF satellite-based albedo product hemispheric reflectance

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Early hydration and mechanical properties of high-belite sulfoaluminate cement blended with ferrite

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Construction and Building Materials 2025年 489卷

作者： Hu, Mengting Liao, Yishun Cai, Zhonghui Meng, Caisheng Wan, Shihui Tang, Shengwen Qunaynah, Siraj Al Wang, Kejin School of Urban Construction Wuhan University of Science and Technology Wuhan430065 China Hubei Provincial Engineering Research Center of Urban Regeneration Wuhan430065 China China West Construction Group Co. Ltd Wuhan430074 China State Key Laboratory of Water Resources Engineering and Management Wuhan University Wuhan430072 China Department of Civil Construction and Environmental Engineering Iowa State University AmesIA50011 United States

In this study, the effect of ferrite incorporation on the hydration of high-belite sulfoaluminate cement was investigated. The fluidity, compressive strength, heat of hydration, electrical resistivity, and scanning electron microscopy-energy dispersive X-ray spectroscopy results of the cement paste were evaluated. The results indicated that the incorporation of ferrite promoted the formation of strätlingite and accelerated the reaction of belite during the late stage. When the content of ferrite increased to 25 %, the heat release of the cement pastes at, 3 d decreased by 6 %, the compressive strength of the cement pastes at 28 d decreased by 65.6 %. The early hydration products of ferrite in cement pastes have multidirectional crystals (ettringite) and gels (calcium silicate hydrate). The Fe element was found to alter the structure of ettringite, resulting in an antler-like shape. Furthermore, according to the EDS results, ettringite contained more Fe, indicating that Fe migration was inclined toward the crystals. Thermodynamic modeling demonstrated that the growth of ettringite and strätlingite in the paste was promoted, which was consistent with the experimental results. © 2025 Elsevier Ltd

关键词： Compressive strength

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Approaching highly stable optoelectronic device operation at elevated temperature by locking backbone torsion of conjugated polymers

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Matter 2025年

作者： Cao, Zhiqiang Li, Zhaofan Awada, Angela Tolba, Sara A. Mooney, Madison Wang, Yangyang Chiu, Yu-Cheng Rondeau-Gagné, Simon Xia, Wenjie Gu, Xiaodan School of Polymer Science and Engineering Center for Optoelectronic Materials and Devices The University of Southern Mississippi HattiesburgMS39406 United States Department of Aerospace Engineering Iowa State University AmesIA50011 United States Department of Chemistry and Biochemistry University of Windsor WindsorONN9B3P4 Canada Materials and Nanotechnology Program North Dakota State University FargoND58102 United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Chemical Engineering National Taiwan University of Science and Technology Taipei City10607 Taiwan

The ability of organic electronics to maintain stability at elevated temperatures is crucial for the longevity of optoelectronic devices. However, achieving stable optoelectronic properties for conjugated polymers remains fundamentally challenging. Here, we identify backbone twisting motion as the primary reason for the unstable optoelectronic properties of donor-acceptor (D-A) conjugated polymers, using diketopyrrolopyrrole (DPP)-based polymers as a model system. The backbone thiophene-ring twist transition is responsible for shifts in the band gap and alterations in charge transport properties. The twisting motion of the thiophene induces localization of the intrachain electrons, resulting in reduced charge carrier mobility and a significant blue shift in optical absorption. Additionally, we demonstrated that intramolecular hydrogen bonding interaction within DPP polymers can suppress undesired backbone twisting at elevated temperatures, thereby ensuring a more stable optoelectronic property. Our work offers fundamental insights into the decline in device stability at elevated temperatures and a strategy to enhance device stability. © 2025 The Authors

关键词： Electron mobility

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Improved Measurement of the Evolution of the Reactor Antineutrino Flux and Spectrum at Daya Bay

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Physical Review Letters 2023年第21期130卷 211801-211801页

作者： F. P. An W. D. Bai A. B. Balantekin M. Bishai S. Blyth G. F. Cao J. Cao J. F. Chang Y. Chang H. S. Chen H. Y. Chen S. M. Chen Y. Chen Y. X. Chen J. Cheng Y.-C. Cheng Z. K. Cheng J. J. Cherwinka M. C. Chu J. P. Cummings O. Dalager F. S. Deng Y. Y. Ding M. V. Diwan T. Dohnal D. Dolzhikov J. Dove K. V. Dugas H. Y. Duyang D. A. Dwyer J. P. Gallo M. Gonchar G. H. Gong H. Gong W. Q. Gu J. Y. Guo L. Guo X. H. Guo Y. H. Guo Z. Guo R. W. Hackenburg Y. Han S. Hans M. He K. M. Heeger Y. K. Heng Y. K. Hor Y. B. Hsiung B. Z. Hu J. R. Hu T. Hu Z. J. Hu H. X. Huang J. H. Huang X. T. Huang Y. B. Huang P. Huber D. E. Jaffe K. L. Jen X. L. Ji X. P. Ji R. A. Johnson D. Jones L. Kang S. H. Kettell S. Kohn M. Kramer T. J. Langford J. Lee J. H. C. Lee R. T. Lei R. Leitner J. K. C. Leung F. Li H. L. Li J. J. Li Q. J. Li R. H. Li S. Li S. C. Li W. D. Li X. N. Li X. Q. Li Y. F. Li Z. B. Li H. Liang C. J. Lin G. L. Lin S. Lin J. J. Ling J. M. Link L. Littenberg B. R. Littlejohn J. C. Liu J. L. Liu J. X. Liu C. Lu H. Q. Lu K. B. Luk B. Z. Ma X. B. Ma X. Y. Ma Y. Q. Ma R. C. Mandujano C. Marshall K. T. McDonald R. D. McKeown Y. Meng J. Napolitano D. Naumov E. Naumova T. M. T. Nguyen J. P. Ochoa-Ricoux A. Olshevskiy J. Park S. Patton J. C. Peng C. S. J. Pun F. Z. Qi M. Qi X. Qian N. Raper J. Ren C. Morales Reveco R. Rosero B. Roskovec X. C. Ruan B. Russell H. Steiner J. L. Sun T. Tmej K. Treskov W.-H. Tse C. E. Tull Y. C. Tung B. Viren V. Vorobel C. H. Wang J. Wang M. Wang N. Y. Wang R. G. Wang W. Wang X. Wang Y. Wang Y. F. Wang Z. Wang Z. M. Wang H. Y. Wei L. H. Wei L. J. Wen K. Whisnant C. G. White H. L. H. Wong E. Worcester D. R. Wu Q. Wu W. J. Wu D. M. Xia Z. Q. Xie Z. Z. Xing H. K. Xu J. L. Xu T. Xu T. Xue C. G. Yang L. Yang Y. Z. Yang H. F. Yao M. Ye M. Yeh B. L. Young H. Z. Yu Z. Y. Yu B. B. Yue V. Zavadskyi S. Zeng Y. Zeng L. Zhan C. Zhang F. Y. Zhang H. H. Zhang J. L. Zhang J. W. Zhang Q. M. Zhang S. Q. Zhang X. T. Zhang Y. M. Zhang Y. X. Zhang Y. Y. Zhang Z. J. Zhang Z. P. Zhang Z. Y. Zhang J. Zhao R. Z. Zhao L. Zhou H. L. Zhuang J Sun Yat-Sen (Zhongshan) University Guangzhou University of Wisconsin Madison Wisconsin 53706 Brookhaven National Laboratory Upton New York 11973 Department of Physics National Taiwan University Taipei Institute of High Energy Physics Beijing National United University Miao-Li Department of Engineering Physics Tsinghua University Beijing Shenzhen University Shenzhen North China Electric Power University Beijing Chinese University of Hong Kong Hong Kong Siena College Loudonville New York 12211 Department of Physics and Astronomy University of California Irvine California 92697 University of Science and Technology of China Hefei Charles University Faculty of Mathematics and Physics Prague Joint Institute for Nuclear Research Dubna Moscow Region Department of Physics University of Illinois at Urbana-Champaign Urbana Illinois 61801 Shandong University Jinan Lawrence Berkeley National Laboratory Berkeley California 94720 Department of Physics Illinois Institute of Technology Chicago Illinois 60616 Beijing Normal University Beijing Department of Nuclear Science and Technology School of Energy and Power Engineering Xi’an Jiaotong University Xi’an Wright Laboratory and Department of Physics Yale University New Haven Connecticut 06520 China Institute of Atomic Energy Beijing Guangxi University No. 100 Daxue East Road Nanning Center for Neutrino Physics Virginia Tech Blacksburg Virginia 24061 Institute of Physics National Chiao-Tung University Hsinchu Department of Physics University of Cincinnati Cincinnati Ohio 45221 Department of Physics College of Science and Technology Temple University Philadelphia Pennsylvania 19122 Dongguan University of Technology Dongguan Department of Physics University of California Berkeley California 94720 Department of Physics The University of Hong Kong Pokfulam Hong Kong School of Physics Nankai University Tianjin Department of Physics and Astronomy Shanghai Jiao Tong University Shanghai Laboratory for Particle Physics and Co

Reactor neutrino experiments play a crucial role in advancing our knowledge of neutrinos. In this Letter, the evolution of the flux and spectrum as a function of the reactor isotopic content is reported in terms of the inverse-beta-decay yield at Daya Bay with 1958 days of data and improved systematic uncertainties. These measurements are compared with two signature model predictions: the Huber-Mueller model based on the conversion method and the SM2018 model based on the summation method. The measured average flux and spectrum, as well as the flux evolution with the Pu239 isotopic fraction, are inconsistent with the predictions of the Huber-Mueller model. In contrast, the SM2018 model is shown to agree with the average flux and its evolution but fails to describe the energy spectrum. Altering the predicted inverse-beta-decay spectrum from Pu239 fission does not improve the agreement with the measurement for either model. The models can be brought into better agreement with the measurements if either the predicted spectrum due to U235 fission is changed or the predicted U235, U238, Pu239, and Pu241 spectra are changed in equal measure.

关键词： Neutrino interactions Nuclear reactors Neutrinos Sterile neutrinos Neutrino detectors

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A pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring data

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National science Review 2025年第5期 362-381页

作者： Yurun Lu Dan Liu Zhongming Liang Rui Liu Pei Chen Yitong Liu Jiachen Li Zhanying Feng Lei M.Li Bin Sheng Weiping Jia Luonan Chen Huating Li Yong Wang Center for Excellence in Mathematical Sciences National Center for Mathematics and Interdisciplinary Sciences Hua Loo-Keng Center for Mathematical Sciences Key Laboratory of Management Decision and Information System Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematics University of Chinese Academy of SciencesChinese Academy of Sciences Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of Sciences BGI-Research School of Mathematics South China University of Technology Department of Statistics Department of Biomedical Data Science Bio-X ProgramStanford University Department of Computer Science and Engineering Shanghai Jiao Tong University State Key Laboratory of Cell Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Guangdong Institute of Intelligence Science and Technology Pazhou Laboratory (Huangpu)

Continuous glucose monitoring(CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and effective decision making for diabetes management. Here, we developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of CGM data to represent individual's intrinsic metabolic state and enable clinical applications. During pretraining, CGMformer encodes glucose dynamics including glucose level, fluctuation, hyperglycemia, and hypoglycemia into latent space with self-supervised learning. It shows generalizability in imputing glucose value across five external datasets with different populations and metabolic states(MAE = 3.7 mg/d L). We then fine-tuned CGMformer towards a diverse panel of downstream tasks in the screening of diabetes and its complications using task-specific data, which demonstrated a consistently boosted predictive accuracy over direct fine-tuning on a single task(AUROC = 0.914 for type 2 diabetes(T2D) screening and 0.741 for complication screening). By learning an intrinsic representation of an individual's glucose dynamics,CGMformer classifies non-diabetic individuals into six clusters with elevated T2D risks, and identifies a specific cluster with lean body-shape but high risk of glucose metabolism disorders, which is overlooked by traditional glucose measurements. Furthermore, CGMformer achieves high accuracy in predicting an individual's postprandial glucose response with dietary modelling(Pearson correlation coefficient = 0.763)and helps personalized dietary recommendations. Overall, CGMformer pretrains a transformer neural network architecture to learn an intrinsic representation by borrowing information from a large amount of daily glucose profiles, and demonstrates predictive capabilities fine-tuned towards a broad range of downstream applications, holding promise for the ear

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Agricultural All-Terrain Vehicle Safety: Hazard Control Methods Using the Haddon Matrix

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Journal of Agromedicine 2021年第4期26卷 420-435页

作者： Farzaneh Khorsandi Paul Ayers Gerene Denning Charles Jennissen Dee Jepsen Melvin Myers Stephen Oesch Michael Pate David J. White a Agricultural Safety and Health Engineering Assistant Specialist in Cooperative Extension Defpartment of Biological and Agricultural Engineering University of California Davis b Professor Department of Biosystems Engineering and Soil Science University of Tennessee Knoxville c Emeritus Research Scientist in Emergency Medicine Department of Emergency Medicine University of Iowa Carver College of Medicine d MD Clinical Professor of Pediatrics and Emergency Medicine Department of emergency medicine University of Iowa Carver College of Medicine e Associate Professor / State Agricultural Safety Leader Department of Food Agricultural and Biological Engineering agricultural safety and health program The Ohio State University f Associate Professor (Adjunct) Environmental Health Emory University Emory Rollins School of Public Health Atlanta Georgia g Retired highway and motor vehicle safety attorney h Associate Professor Applied Sciences Technology & Education Agricultural Systems Technology and Education program Utah State University i PhD Professor of Social and Behavioral Health Sciences College of Public Health and Human Sciences School of Social and Behavioral Health Sciences and Extension 4-H Youth Development Oregon State University

ABSTRACTABSTRACTAll-terrain vehicle (ATV) crashes are one of the leading causes of death and injury in agriculture across the United states. Use of ATVs is highly prevalent in rural areas and has seen an increase in their application for a variety of agricultural work tasks. Empirical research on the use of these vehicles for agricultural tasks and associated injuries is limited, especially in the United states. Moreover, little is known about the risk factors associated with ATV-related injuries while doing farm work. A comprehensive review was conducted to evaluate the current injury burden of ATV use in agriculture, the need for future research, and possible solutions related to agricultural ATV safety. Potential injury prevention approaches are evaluated based on the hierarchy of control, including elimination or substitution (using side-by-side vehicles instead of ATVs or modifying ATV design), engineering control (operator protection devices), administrative authority (regulations and standards in the United states and around the world), training, and use of personal protective equipment. In addition, vehicle nomenclature, risk factors, and crash mechanisms are reviewed. Recommendations to decrease the likelihood of ATV crashes and injuries are provided.

关键词： Agriculture all-terrain vehicle ATV quad bike safety

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Impacts of horizontal resolution and downscaling on the USLE LS factor for different terrains

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International Soil and Water Conservation Research 2020年第4期8卷 363-372页

作者： Chunmei Wang Linxin Shan Xin Liu Qinke Yang Richard M.Cruse Baoyuan Liu Rui Li Hongming Zhang Guowei Pang Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity Northwest UniversityXi'an710127PR China College of Urban and Environmental Sciences Northwest UniversityXi'an710127PR China Key Laboratory of National Forestry Administration on Ecological Hydrology and Disaster Prevention in Arid Regions Xi'an University of TechnologyXi'an 710048PR China Department of Agronomy Iowa State UniversityAmesIA50011USA Xi'an Institute of Prospecting and Mapping Xi'an710054PRChina Faculty of Geographical Science Beijing Normal UniversityBeijing100875PR China State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau Institute of Soil and Water ConservationNorthwest A&F UniversityYangling712100ShaanxiPR China College of Information Engineering Northwest A&F UniversityYanglingShaanxi712100China

Slope length and slope steepness are critical topographic factors(L and S)in the Universal Soil Loss Equation(USLE)and Chinese Soil Loss Equation(CSLE)for soil erosion *** slope length and slope gradient are potentially sensitive to spatial resolution when calculated in a GIS *** resolution effect on the LS factor and approaches suitable for improving the LS factor at a coarse resolution have not been well *** address this problem,the LS factor at 5-m and 30-m resolution in twenty-four watersheds with various terrains was *** a downscale model based on matching of the lower resolution LS cumulative frequency curves to a higher resolution("Histogram Matching"method)was tested for its potential to improve LS factor estimation *** the larger relief mountainous area,compared to 5-m resolution,the 30-m resolution generated LS was generally overestimated by more than 20%and in lower relief areas underestimated by more than 15%.This bias is less than 10%in medium relief *** downscale model improved LS factor estimates compared to the 30-m resolution estimate by more than 10%when comparing frequency distribution curves and more than 20%in mean values in larger relief *** downscale model worked well in all regions except for the low relief areas,which intuitively are the low soil erosion potential *** results of this research help quantify the uncertainty in soil erosion estimates and may ultimately help to improve the assessment of soil erosion through its impact on LS factor estimates,especially at regional and global scales.

关键词： LS factor SRTM Resolution Downscale USLE

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Towards a Visual-Language Foundation Model for Computational Pathology

arXiv

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arXiv 2023年

作者： Lu, Ming Y. Chen, Bowen Williamson, Drew F.K. Chen, Richard J. Liang, Ivy Ding, Tong Jaume, Guillaume Odintsov, Igor Zhang, Andrew Le, Long Phi Gerber, Georg Parwani, Anil V. Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States CambridgeMA United States Health Sciences and Technology Harvard-MIT CambridgeMA United States Harvard John A. Paulson School of Engineering And Applied Sciences Harvard University CambridgeMA United States Department of Pathology Wexner Medical Center Ohio State University ColumbusOH United States Harvard Data Science Initiative Harvard University CambridgeMA United States

The accelerated adoption of digital pathology and advances in deep learning have enabled the development of powerful models for various pathology tasks across a diverse array of diseases and patient cohorts1–13. However, model training is often difficult due to label scarcity in the medical domain and the model’s usage is limited by the specific task and disease for which it is trained1–3, 14, 15. Additionally, most models in histopathology leverage only image data, a stark contrast to how humans teach each other and reason about histopathologic entities. We introduce CONtrastive learning from Captions for Histopathology (CONCH), a visual-language foundation model developed using diverse sources of histopathology images, biomedical text, and notably over 1.17 million image-caption pairs via task-agnostic pretraining. Evaluated on a suite of 13 diverse benchmarks, CONCH can be transferred to a wide range of downstream tasks involving either or both histopathology images and text, achieving state-of-the-art performance on histology image classification, segmentation, captioning, text-to-image and image-to-text retrieval. CONCH represents a substantial leap over concurrent visual-language pretrained systems for histopathology, with the potential to directly facilitate a wide array of machine learning-based workflows requiring minimal or no further supervised fine-tuning. © 2023, CC BY-NC-ND.

关键词： Pathology

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