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SSRN 2024年

作者： Park, Chan Joo Choi, Kyu Sung Park, Jaeseok Choi, Seung Hong Hwang, Inpyeong Shin, Taehoon Division of Mechanical and Biomedical Engineering Ewha Womans University Seoul Korea Republic of Graduate Program in Smart Factory Ewha Womans University Seoul Korea Republic of Department of Radiology Seoul National University Hospital Seoul Korea Republic of Department of Biomedical Engineering Sungkyunkwan University Suwon Korea Republic of Department of Intelligent Precision Healthcare Convergence Sungkyunkwan University Suwon Korea Republic of Department of Radiology Seoul National University Hospital Seoul National University College of Medicine Seoul Korea Republic of

Time-resolved contrast-enhanced magnetic resonance angiography (TR-MRA) is an established technique used to capture the movement of a contrast bolus through the vascular system. However, in the cerebral vasculature, which has a short arteriovenous transit time, venous overlay can occur even in the image of the peak arterial phase, making it challenging to obtain pure artery-only angiograms. This study aimed to develop conditional generative neural networks to generate pure angiograms with enhanced arterial contrast using clinical cerebral TR-MRA data. To achieve this, we proposed a preprocessing algorithm that synthesizes images with optimal arterial contrast through eigen-image analysis utilizing contrast dynamics. This synthetic image and temporal maximum-intensity-projection of TR-MRA served as the target and source images, respectively for training conditional generative adversarial network (GAN) and denoising diffusion probabilistic model (DDPM). The results showed that the conditional DDPM achieved substantially higher arterial contrast compared to raw TR-MRA, as confirmed by the relative artery-vein contrast ratio (0.9889 ± 0.0049 vs. 0.8265 ± 0.0502) and the artery-muscle contrast ratio (0.9825 ± 0.0038 vs. 0.8806 ± 0.0225). The conditional DDPM outperformed conditional GAN in quality of visualization of arteries, as confirmed by the qualitative visibility score (4.07 ± 0.49 vs. 3.82 ± 0.70). Furthermore, we demonstrated the feasibility of applying the trained generative models to single-phase contrast-enhanced MRA images with high spatial resolution. © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Compatibility of Tio2 Reflective Material with Ce:Gagg Scintillators in Harsh Environments

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SSRN 2024年

作者： Park, Chansun Elmughrabi, Abdallah Melis, Alima Cho, Shinhaeng Kim, Sangsu Yeom, Jung-Yeol Global Health Technology Research Center Korea University Seoul02841 Korea Republic of Department of Bioengineering Korea University Seoul02841 Korea Republic of Interdisciplinary Program in Precision Public Health Korea University Seoul02841 Korea Republic of Department of Radiation Oncology Chonnam National University Medical School Gwangju61469 Korea Republic of School of Biomedical Engineering Korea University Seoul02841 Korea Republic of

In this study, we developed Ce:GAGG ceramic scintillators (containing 5 × 5 × 2 mm3 Ce:GAGG single crystals) encapsulated by different light reflectors, namely, Teflon (400 μm), enhanced specular reflector (ESR) film (65 μm), and a titanium dioxide (TiO2) reflector with different thicknesses and measured their γ-ray spectra under irradiation by a 137Cs radioactive source. The reflectance of a 100-µm-thick (optimal thickness) TiO2 layer surpassed those of the other two counterparts and resulted in a high light-collection efficiency. In order to assess the temperature dependence of the reflectors, samples were heated up to 300 °C in air. The measured reflectance decreased with the increasing temperature, and the color of the reflector’s surface changed. The observed variations in the surface morphology, measured by scanning electron microscopy, corresponded to the determined reflectance of the TiO2 layer. Upon heating above 200 °C in air, the TiO2 layer decomposed into other oxides, with a simultaneous increase in its surface porosity. On the other hand, Ce:GAGG single crystal sample heated at 300 °C in vacuum showed only a negligible color variation, retained 80.3% of their initial reflectance, and exhibited an energy resolution of 12.8%. This implies that oxidation may be the cause of performance degradation, and a passivation layer may allow the use of reflectors at high temperatures. This was verified when a TiO2 layer covered with a heat-resistant epoxy exhibited comparable performance by isolating the reflecting from the air. The results obtained in this study suggest that a TiO2 layer can be used as a long-wavelength reflector in oxide and other types of scintillators for efficient radiation detection in harsh environments, including air-free environments, such as in aerospace, under vacuum conditions or when used in conjunction with a protective passivation layer. © 2024, The Authors. All rights reserved.

关键词： Titanium dioxide

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Technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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Optimal Design of Investment Casting System for a Stuffing Box with Pedestal of Centrifugal Pump: Computer Simulations and Experimental Verification

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Journal of the Chinese Society of Mechanical Engineers, Transactions of the Chinese Institute of Engineers, Series C/Chung-Kuo Chi Hsueh Kung Ch'eng Hsuebo Pao 2020年第5期41卷 551-563页

作者： Kuo, Jenn-Kun Huang, Pei-Hsing Tsai, Yu-Ting Zhong, Qi Chou, Chuen-Shii Department of Greenergy National University of Tainan Tainan70005 Taiwan Department of Mechanical Engineering National Yunlin University of Science and Technology Yunlin64002 Taiwan Bachelor's Program in Precision System Design Feng Chia University Taichung40724 Taiwan Department of Mechanical Engineering National Pintung University of Science and Technology Pingtung91201 Taiwan

The stuffing box with pedestal (SBP) is a major component in centrifugal pumps (CPs). It is responsible for the transfer of load generated in the CP to the baseplate of the pump and finally to the ground. SBPs are usually fabricated using investment casting;however, this process is prone to residual casting defects, which can reduce mechanical strength and increase the likelihood of crack formation following long-term usage. In this study, the AnyCasting numerical simulation software package was used to optimize the investment parameters for the casting of an SBP in SUS304 alloy. Optimization efforts targeted the shrinkage cavity (based on retained melt modulus) and porosity defects (based on Niyama criterion). The results of preliminary simulations and experiments informed the establishment of five casting schemes for subsequent simulation. We used virtual thermo-dynamic sensors to determine the rate and direction of solidification in the mold cavity, and also investigated variations in the velocity of the molten metal at the inlets of the gating system as a function of time. The optimal casting parameters derived in simulations were assessed by fabricating an SBP via investment casting in a domestic foundry. Nondestructive testing based on X-ray analysis revealed none of the detrimental defects commonly associated with this type of casting. © 2020, Chinese Mechanical engineering Society. All right reserved.

关键词： Investment casting

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Generating Lymphoma Variability Through Adversarial Generation Models: A Limited Resource Approach for Synthetic Histopathology Image Generation and Classification

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SSRN 2024年

作者： Tiwari, Ashutosh Saudarshan, Harsh Krisnawati, Dyah Ika Khafid, Muhamad Wardani, Erika Martining Mutalik, Chinmaya Chen, Chi-Long Kuo, Tsung-Rong International Ph.D. Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Department of Computer Science and Engineering Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur495509 India Department of Nursing Faculty of Nursing and Midwifery Universitas Nahdlatul Ulama Surabaya East Java Surabaya60237 Indonesia Graduate Institute of Nanomedicine and Medical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Department of Pathology School of Medicine College of Medicine and Taipei Medical University Hospital Taipei Medical University Taipei11031 Taiwan International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan Precision Medicine and Translational Cancer Research Center Taipei Medical University Hospital Taipei11031 Taiwan Tulane University United States Graduate Institute of Clinical Medicine Taiwan

Lymphomas, a diverse group of blood cancers originating from the lymphatic system, present significant challenges for diagnosis and treatment due to their morphological and genetic differences. These variability obscures the development of personalized therapies and impacts disease outcomes. The scarcity of high-quality, diverse images of lymphoma cells is further hindered by patient privacy concerns, the rarity of certain types, and logistical challenges of collecting and processing biomedical images. Additionally, the costs and technical difficulties of microscopic imaging limit the scope of research. This paper addresses these challenges by utilizing generative adversarial networks (GANs) to generate synthetic yet realistic images of lymphoma histopathology and classify them in a low resource setting. Focusing specifically on chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma (MCL), we employed GANs to enhance the understanding of lymphoma variability and support advancements in digital cancer biology and pathology. This dual approach of image generation and classification aims to improve disease diagnosis and prognosis, ultimately contributing to better patient outcomes. © 2024, The Authors. All rights reserved.

关键词： Lung cancer

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precision medicine: Beyond AI

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Computer Methods and programs in Biomedicine Update 2024年 5卷

作者： Filetti, Marco Petti, Manuela Farina, Lorenzo Phase 1 Unit Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy Department of Experimental Medicine Sapienza University of Rome Rome Italy Department of Computer Control Management Engineering Sapienza University of Rome Via Ariosto 25 Rome 00185 Italy PhD. Program in “network oncology and precision medicine” Sapienza University of Rome Italy

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Study of Mice Ovaries using Optical Coherence Tomography

Study of Mice Ovaries using Optical Coherence Tomography

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Latin America Optics and Photonics Conference, LAOP 2022

作者： Amaral, Marcello Magri Sun, Aixia Li, Yilin Wang, Ping Jiao, Zexu Zhou, Chao Biomedical Engineering Department Washington University in St Louis 1 Brookings Dr St LouisMO United States Universidade Brasil Rua Carolina Fonseca 235 SP Sao Paulo Brazil Precision Health Program Michigan State University East LansingMI United States Department of Radiology College of Human Medicine Michigan State University East LansingMI United States Department of Obstetrics and Gynecology University of Texas Southwestern Medical Center DallasTx United States

We investigate the age-related follicle and oocyte morphological differences using Optical Coherence Tomography (OCT). The oocyte's development stages were observed and discussed. OCT technique can provide a real-... 详细信息

ISBN: (纸本)9781957171135

关键词： Optical tomography

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NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG

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arXiv 2024年

作者： Lee, Cheol-Hui Kim, Hakseung Han, Hyun-Jee Jung, Min-Kyung Yoon, Byung C. Kim, Dong-Joo The Department of Brain and Cognitive Engineering Korea University Seoul Korea Republic of The Department of Pharmacology University of Cambridge Cambridge United Kingdom The Department of Radiology Stanford University School of Medicine VA Palo Alto Health Care System Palo AltoCA United States The Department of Neurology Korea University College of Medicine Seoul Korea Republic of Interdisciplinary Program in Precision Public Health Korea University Seoul Korea Republic of

The classification of sleep stages is a pivotal aspect of diagnosing sleep disorders and evaluating sleep quality. However, the conventional manual scoring process, conducted by clinicians, is time-consuming and prone to human bias. Recent advancements in deep learning have substantially propelled the automation of sleep stage classification. Nevertheless, challenges persist, including the need for large datasets with labels and the inherent biases in human-generated annotations. This paper introduces NeuroNet, a self-supervised learning (SSL) framework designed to effectively harness unlabeled single-channel sleep electroencephalogram (EEG) signals by integrating contrastive learning tasks and masked prediction tasks. NeuroNet demonstrates superior performance over existing SSL methodologies through extensive experimentation conducted across three polysomnography (PSG) datasets. Additionally, this study proposes a Mamba-based temporal context module to capture the relationships among diverse EEG epochs. Combining NeuroNet with the Mamba-based temporal context module has demonstrated the capability to achieve, or even surpass, the performance of the latest supervised learning methodologies, even with a limited amount of labeled data. This study is expected to establish a new benchmark in sleep stage classification, promising to guide future research and applications in the field of sleep analysis. The source code is available at https://***/dlcjfgmlnasa/NeuroNet Copyright © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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High performance gamma-ray spectrometer based on Cs3Cu2I5 single crystal grown by room-temperature vacuum solvent-evaporation method

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Journal of Alloys and Compounds 2025年 1027卷

作者： Seo, Jiwon Song, Seungho Park, Minhwan Byun, Jangwon Park, Beomjun Park, Chansun Jang, Mee Yeom, Jung-Yeol Dept. of Health and Safety Convergence Science Korea University Seoul02841 Korea Republic of Interdisciplinary Program in Precision Public Health Korea University Seoul02841 Korea Republic of Dept. of Bioengineering Korea University Seoul02841 Korea Republic of Dept. of Materials Science and Engineering Korea University Seoul02841 Korea Republic of The Institute for High Technology Materials and Devices Korea University Seoul02841 Korea Republic of Global Health Technology Center Korea University Seoul02841 Korea Republic of Daejeon34057 Korea Republic of School of Biomedical Engineering Korea University Seoul02841 Korea Republic of

In this study, the properties of Cs3Cu2I5 (CCI) crystals grown using the Slow Vacuum Evaporation Technique (SVET) were examined. Crystals were grown at room temperature (300 K, 27 °C) under vacuum conditions, producing single crystals with maximum diameter of 13 mm over 23 days. We confirmed excellent crystallinity via X-ray diffraction (XRD), compositional uniformity via X-ray photoelectron spectroscopy (XPS), and homogeneity via electron probe micro analyzer (EPMA). UV-Vis measurements indicated a high transmittance of 83 % and a bandgap of 3.64 eV. The Stokes shift of 110 nm, smaller than previously reported values, contributed to faster decay times and improved low-energy detection feasibility. Time-resolved photoluminescence (TR-PL) showed a rapid decay component (4.1 ns), accounting for 95.11 % of the total decay, demonstrating superior properties compared to existing reports. The gamma-ray detection capabilities of the CCI crystals were evaluated using six radioactive sources (Am-241, Ba-133, Co-57, Na-22, Cs-137, and Mn-54), achieving excellent energy resolution, including 5.2 % at 662 keV. The crystals displayed a highly linear response to gamma radiation (R² = 0.99998) with a light yield of 55,613 photons/MeV. Moreover, assessments of multiple crystals grown under identical conditions showed consistent performance with a coefficient of variation (CV) of 1.23 %. These findings confirm that SVET is a highly effective and cost-efficient method for producing high-quality CCI crystals, offering significant advantages over other growth techniques. © 2025 Elsevier B.V.

关键词： Gamma ray spectrometers

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A Recurrence Plot-Based Graph Convolutional Network for Time Series Classification

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SSRN 2024年

作者： Lee, Junghye Kang, Hyewon Lee, Taek-Ho Department of Intelligent Precision Healthcare Convergence Sungkyunkwan University 2066 Seobu-ro Jangan-gu Gyeonggi-do Suwon-si16419 Korea Republic of Technology Management Economics and Policy Program Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Institute of Engineering Research Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Graduate School of Engineering Practice Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of

Over the past decades, time series classification (TSC) has become an essential task in many fields, leading to a plethora of TSC deep learning algorithms. The effectiveness of these algorithms largely depends on the quality of input representations. Among various representations, a recurrence plot (RP), a binary-encoded matrix, stands out as a powerful input representation. The RP encapsulates a topological recurrence, the unique property of time series data. This property is characterized by the fact that each point in a certain region of its phase space returns arbitrarily close to its initial position infinitely often. Despite its grid-like appearance, the essential information of the RP lies in its inherently intricate topological recurrence patterns. Recently, the RP has been widely used with a convolutional neural network (CNN) for TSC, treating the RP as grid-like data. However, the topological recurrence patterns can often be overlooked when using CNNs with their 2D convolution operation designed for Euclidean space. In this study, we propose an approach named RP-GCN that combines the RP with a graph convolutional network (GCN) to exploit the topological recurrence inherent in the RP, thereby improving TSC performance. Our RP-GCN transforms a multivariate time series into state matrices and RPs, redefining them as node feature matrices and adjacency matrices, respectively. We validated the performance of our RP-GCN on 35 benchmark multivariate time series classification datasets and found that it exhibited promising performance in terms of accuracy and inference time. © 2024, The Authors. All rights reserved.

关键词： Convolution

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