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arXiv 2024年

作者： Rudie, Justin Amoah, Sylvester Wang, Xiaoxin Kumar, Rajesh Abernathy, Grey Akwabli, Steven Grant, Perry C. Liu, Jifeng Li, Baohua Du, Wei Yu, Shui-Qing Material Science and Engineering University of Arkansas FayettevilleAR72701 United States Department of Electrical Engineering and Computer Science University of Arkansas FayettevilleAR72701 United States Thayer School of Engineering Dartmouth College HanoverNH03755 United States Arktonics LLC 1339 S. Pinnacle Dr. FayettevilleAR72701 United States Materials Science & Engineering Program University of Arkansas FayettevilleAR72701 United States Institute for Nanoscience and Engineering University of Arkansas FayettevilleAR72701 United States

We demonstrate monolithically grown germanium-tin (GeSn) on silicon avalanche photodiodes (APDs) for infrared light detection. A relatively thinner Ge buffer design was adopted to allow effective photo carriers to transport from the GeSn absorber to the Si multiplication layer such that clear punch-through behavior and a saturated primary responsivity of 0.3 A/W at 1550 nm were observed before avalanche breakdown in GeSn/Si APDs for the first time. The spectral response covers 1500 to 1700 nm. The measured punch-through and breakdown voltages are 15 and 17 V, respectively. Undisputed multiplication gain was obtained with the maximum value of 4.5 at 77 K, and 1.4 at 250 K, directly in reference to the saturated primary responsivity from the same device rather than a different GeSn p-i-n photodiode in previous reports. A peak responsivity was measured as 1.12 A/W at 1550 nm and 77 K. © 2024, CC0.

关键词： Avalanche photodiodes

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Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using a Deep Learning Approach

Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using ...

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International Symposium on Applied science 2024, ISAS 2024

作者： Raza, Aquib Ching, Congo Tak Shing Liu, Peng-Ta Phan, Thien-Luan Tran, Ngoc-Luan Nguyen, Ha Anh T. Rameez, Mohammad Li, Hung-Chung Linh, Dao Nguyen Phuong Van Hieu, Nguyen Tran, Trung Nghia National Taiwan University Taipei City10673 Taiwan Research Center for Information Technology Academia Sinica Taipei City11529 Taiwan Graduate Institute of Biomedical Engineering National Chung Hsing University Taichung402 Taiwan Department of Electrical Engineering National Chi Nan University Nantou County Taiwan Advanced Plant and Food Crop Biotechnology Center National Chung Hsing University Taichung402 Taiwan International Doctoral Program in Agriculture National Chung Hsing University Taichung402 Taiwan Fall Prevention Center Department of Physical Medicine & Rehabilitation Changhua Christian Hospital Changhua500 Taiwan Department of Physics and Electronic Engineering University of Science Vietnam National University - Ho Chi Minh City Ho Chi Minh City70000 Viet Nam Department of Computer Science University of York York HeslingtonYO10 5DD United Kingdom Undergraduate Program of Intellectual Creativity Engineering National Chung Hsing University Taichung402 Taiwan Ho Chi Minh City72409 Viet Nam HCMUT Vietnam National University Ho Chi Minh City Thu Duc Ho Chi Minh City71308 Viet Nam

This research introduces a deep learning framework that combines convolutional neural networks with autoencoders to improve the diagnostic accuracy of knee osteoarthritis. The study utilized a publicly available dataset from the Mendeley Data repository, where knee joint images were preprocessed using Contrast Limited Adaptive Histogram Equalization to enhance contrast and ensure consistent image sizing. Initially, an autoencoder was employed in an unsupervised manner to learn compressed representations of the data. These representations were then used to train a CNN to classify knee images into 'healthy' and 'osteoarthritic' categories. The model was trained using an 80:20 split for training and testing data, incorporating early stopping and data augmentation techniques to prevent overfitting and enhance generalization. Performance metrics such as accuracy, precision, recall, and the area under the Receiver Operating Characteristic curve (ROCAUC) were used to evaluate the model, resulting in a high test accuracy of 98.95%. The high precision and recall rates further confirmed the model's robustness in diagnosing KOA. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to identify critical regions in the images that influenced the model's decisions. Additionally, t-Distributed Stochastic Neighbor Embedding (t-SNE) was used to visualize the high-dimensional encoded features in a two-dimensional space, providing deeper insights into how the model differentiates between classes. The promising results of this deep learning approach suggest its potential for broader applications in medical imaging diagnostics. By providing healthcare professionals with a tool that enhances diagnostic accuracy and operational efficiency, this framework lays the foundation for developing scalable, AI-driven diagnostic systems for various complex medical conditions. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Joints (anatomy)

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Prospective evaluation of data-driven models to predict daily risk of Clostridioides difficile infection at 2 large academic health centers (vol 2, pg 1, 2022)

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INFECTION CONTROL & HOSPITAL EPIDEMIOLOGY 2023年第10期44卷 1705-1705页

作者： Kamineni, Meghana Otles, Erkin Oh, Jeeheh Rao, Krishna Young, Vincent B. Li, Benjamin Y. West, Lauren R. Hooper, David C. Shenoy, Erica S. Guttag, John G. Wiens, Jenna Makar, Maggie Electrical Engineering and Computer Science Department Massachusetts Institute of Technology Cambridge Massachusetts Medical Scientist Training Program University of Michigan Medical School Ann Arbor Michigan Department of Industrial and Operations Engineering University of Michigan College of Engineering Ann Arbor Michigan Division of Computer Science and Engineering University of Michigan College of Engineering Ann Arbor Michigan Department of Internal Medicine Division of Infectious Diseases University of Michigan Medical School Ann Arbor Michigan Infection Control Unit Massachusetts General Hospital Boston Massachusetts Division of Infectious Diseases Massachusetts General Hospital Boston Massachusetts Harvard Medical School Boston Massachusetts

Many data-driven patient risk stratification models have not been evaluated prospectively. We performed and compared the prospective and retrospective evaluations of 2 Clostridioides difficile infection (CDI) risk-prediction models at 2 large academic health centers, and we discuss the models’ robustness to data-set shifts.

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Mechanistic Modeling of Copper Corrosions in Datacenter Environments

SSRN

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SSRN 2023年

作者： Zhang, Rui Zhang, Jianshun Schmidt, Roger Gilbert, Jeremy L. Department of Mechanical and Aerospace Engineering College of Engineering and Computer Science Syracuse University 263 Link Hall SyracuseNY13244 United States Buildings and Transportation Science Division Oak Ridge National Laboratory Oak RidgeTN37830 United States SyracuseCoE New York Center of Excellence in Environmental and Energy Systems Syracuse University 727 E. Washington Street SyracuseNY13244 United States Department of Mechanical and Aerospace Engineering College of Engineering and Computer Science Syracuse University 212 Link Hall SyracuseNY13244 United States Department of Bioengineering Clemson University Clemson United States Medical University of South Carolina Bioengineering Program CharlestonSC29425 United States

To evaluate the reliability of information technology equipment under various thermal and air-pollution conditions, a mechanistic model based on multi-ion transport and chemical reactions was developed. The model was used to predict Cu corrosion caused by Cl2-containing pollutant mixtures. It also accounted for the effects of temperature (25°C and 28°C), relative humidity (50%, 75%, and 95%), and synergism. It also identified higher air temperature as a corrosion barrier and higher relative humidity as a corrosion accelerator, which agreed well with the experimental results. The average root mean square error of the prediction was 13.7 Å. © 2023, The Authors. All rights reserved.

关键词： Copper corrosion

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Seropill: Novel Minimally Invasive Ingestible Capsule for Serotonin Sensing in the GI Tract

Seropill: Novel Minimally Invasive Ingestible Capsule for Se...

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International Conference on Solid State Sensors and Actuators (TRANSDUCERS)

作者： Michael A. Straker Joshua A. Levy Justin M. Stine Jinjing Han Luke A. Beardslee Reza Ghodssi Fischell Department of Bioengineering University of Maryland College Park MD USA Institute for Systems Research University of Maryland College Park MD USA Fischell Institute for Biomedical Devices University of Maryland College Park MD USA Department of Materials Science and Engineering University of Maryland College Park MD USA Department of Electrical and Computer Engineering University of Maryland College Park MD USA

This paper reports the first serotonin (5-HT) sensing ingestible capsule allowing minimally invasive real-time in vivo detection of luminally released serotonin in the gastrointestinal (GI) tract. The system utilizes a surface-mounted sensor with an Au-carbon nanotube (CNT) working electrode, Au counter electrode, and Ag/AgCl reference electrode. The capsule uses previously reported electronics packaged in a 3D-printed ASA shell to perform cyclic voltammetry, allowing the detection and quantification of local serotonin concentration with a dynamic range of 2-10 µM. This device marks a step toward wireless ingestible systems for real-time evaluation of gastrointestinal microbiome metabolites in vivo.

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Gallium Oxide as Photonic Integrated Platforms in the UV–Visible Spectra

Gallium Oxide as Photonic Integrated Platforms in the UV–Vi...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Jingan Zhou Xuan Zhao Rui Xu Xiang Zhang Kai Fu Tao Li Mingfei Xu Shisong Luo Ziyi He Jacob T. Robinson Hanyu Zhu Pulickel M. Ajayan Xiaodong Zhang Yuji Zhao Department of Electrical and Computer Engineering Rice University Houston TX USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX USA Department of Materials Science and NanoEngineering Rice University Houston TX USA School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona USA Nanofabrication Facility Suzhou Institute of Nano-Tech and Nano-Bionics Chinese Academy of Sciences Suzhou China

We report the Ga 2 O 3 as photonic integrated platforms and its nonlinear optical effects in the UV–visible spectra. A low propagation loss of 3.7 dB/cm and second-order susceptibility of χ (2) =4.89×10 -3 pm... 详细信息

ISBN: (纸本)9781957171258

关键词： Material properties Nonlinear effects Nonlinear susceptibility Optical components Thin films Visible light communications

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Imaging of Nanoscale Light Confinement in Plasmonic Nanoantennas by Brownian Optical Microscopy (vol 14, pg 7666, 2020)

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ACS NANO 2020年第9期14卷 12261-12261页

作者： Lee, Yeon Ui Wisna, G. Bimananda M. Hsu, Su-Wen Zhao, Junxiang Lei, Ming Li, Shilong Tao, Andrea R. Liu, Zhaowei Department of Electrical and Computer Engineering University of California San Diego 9500 Gilman Drive La Jolla California 92093 United States Material Science and Engineering Program University of California San Diego 9500 Gilman Drive La Jolla California 92093 United States Department of NanoEngineering University of California San Diego 9500 Gilman Drive La Jolla California 92093 United States Department of Electrical and Computer Engineering University of California San Diego 9500 Gilman Drive La Jolla California 92093 United States Department of Electrical and Computer Engineering University of California San Diego 9500 Gilman Drive La Jolla California 92093 United States Department of Electrical and Computer Engineering University of California San Diego 9500 Gilman Drive La Jolla California 92093 United States Material Science and Engineering Program University of California San Diego 9500 Gilman Drive La Jolla California 92093 United States Department of NanoEngineering University of California San Diego 9500 Gilman Drive La Jolla California 92093 United States Department of Electrical and Computer Engineering University of California San Diego 9500 Gilman Drive La Jolla California 92093 United States Material Science and Engineering Program University of California San Diego 9500 Gilman Drive La Jolla California 92093 United States

The strongly enhanced and confined subwavelength optical fields near plasmonic nanoantennas have been extensively studied not only for the fundamental understanding of light-matter interactions at the nanoscale but also for their emerging practical application in enhanced second harmonic generation, improved inelastic electron tunneling, harvesting solar energy, and photocatalysis. However, owing to the deep subwavelength nature of plasmonic field confinement, conventional optical imaging techniques are incapable of characterizing the optical performance of these plasmonic nanoantennas. Here, we demonstrate super-resolution imaging of ∼20 nm optical field confinement by monitoring randomly moving dye molecules near plasmonic nanoantennas. This Brownian optical microscopy is especially suitable for plasmonic field characterization because of its capabilities for polarization sensitive wide-field super-resolution imaging.

关键词： super-resolution imaging plasmonic nanoantennas hot spot imaging Brownian optical microscopy fluorescence enhancement nanophotonics plasmonics

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An Insight Into Neurodegeneration: Harnessing Functional MRI Connectivity in the Diagnosis of Mild Cognitive Impairment

An Insight Into Neurodegeneration: Harnessing Functional MRI...

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17th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2024

作者： Han, Shuning Sun, Zhe Zhao, Kanhao Duan, Feng Caiafa, Cesar F. Zhang, Yu Solé-Casals, Jordi Data and Signal Processing Research Group University of Vic-Central University of Catalonia Catalonia Vic08500 Spain Image Processing Research Group RIKEN Center for Advanced Photonics Riken Saitama Wako-Shi Japan Faculty of Health Data Science Juntendo University Chiba Urayasu Japan Department of Bioengineering Lehigh University BethlehemPA18015 United States Tianjin Key Laboratory of Brain Science and Intelligent Rehabilitation Nankai University Tianjin China Instituto Argentino de Radioastronomía-CCT La Plata CONICET/ CIC-PBA/ UNLP V. Elisa 1894 Argentina Department of Electrical and Computer Engineering Lehigh University BethlehemPA18015 United States Department of Psychiatry University of Cambridge CambridgeCB20SZ United Kingdom

Alzheimer’s disease is a progressive form of memory loss that worsens over time. Detecting it early, when memory issues are mild, is crucial for effective interventions. Recent advancements in computer technology, specifically Graph Convolutional Networks (GCNs), have proven to be powerful tools for analyzing Magnetic Resonance Imaging (MRI) data comprehensively. In this study, we developed a GCN framework for diagnosing mild cognitive impairment (MCI) by examining the functional connectivity (FC) derived from resting-state functional MRI (rfMRI) data. Our research systematically explored various types and processing methods of FC, evaluating their performance on the OASIS-3 dataset. The experimental results revealed several key findings. On the one hand, the proposed GCN exhibited significantly superior performance over both the baseline GCN and the Support Vector Machine (SVM) models, with statistically significant differences. It attained the highest average accuracy of 80.3% and a peak accuracy of 88.2%. On the other hand, the GCN frame work obtained using individual FCs showed overall slightly better performance than the one using global FCs. However, it is important to note that GCNs using global networks with appropriate connectivity can achieve comparable or even better performance than individual networks in certain cases. Finally, our results also indicate that the connectivity within specific brain regions, such as VIS, DMN, SMN, VAN, and FPC, may play a more significant role in GCN-based MRI classification for MCI diagnosis. These findings significantly contribute to the understanding of neurodegenerative disorders and offer valuable insights into the diverse ap plications of GCNs in brain analysis and disease detection. © 2024 by SCITEPRESS– science and Technology Publications, Lda.

关键词： Neurodegenerative diseases

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Machine Learning Automated Approach for Enormous Synchrotron X-Ray Diffraction Data Interpretation

arXiv

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arXiv 2023年

作者： Zhao, Xiaodong Luo, YiXuan Liu, Juejing Liu, Wenjun Rosso, Kevin M. Guo, Xiaofeng Geng, Tong Li, Ang Zhang, Xin Pacific Northwest National Laboratory RichlandWA99354 United States Department of Chemistry Washington State University Pullman99164 United States Department of Electrical and Computer Engineering University of Rochester New York14627 United States Materials Science and Engineering Program Washington State University Pullman99164 United States

Manual analysis of XRD data is usually laborious and time consuming. The deep neural network (DNN) based models trained by synthetic XRD patterns are proved to be an automatic, accurate, and high throughput method to analysis common XRD data collected from solid sample in ambient environment. However, it remains unknown that whether synthetic XRD based models are capable to solve µ-XRD mapping data for in-situ experiments involving liquid phase exhibiting lower quality with significant artifacts. In this study, we collected µ-XRD mapping data from an LaCl3-calcite hydrothermal fluid system and trained two categories of models to solve the experimental XRD patterns. The models trained by synthetic XRD patterns show low accuracy (as low as 64%) when solving experimental µ-XRD mapping data. The accuracy of the DNN models was significantly improved (90% or above) when training them with the dataset containing both synthetic and small number of labeled experimental µ-XRD patterns. This study highlighted the importance of labeled experimental patterns on the training of DNN models to solve µ-XRD mapping data from in-situ experiments involving liquid phase. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Giant Thermomechanical Bandgap engineering in Quasi-two-dimensional Tellurium

arXiv

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arXiv 2023年

作者： Hussain, Naveed Ahmed, Shehzad Tep, Hüseyin U. Ullah, Kaleem Shahzad, Khurram Wu, Hui Shcherbakov, Maxim R. Department of Electrical Engineering and Computer Science University of California Irvine IrvineCA92697 United States State Key Laboratory of New Ceramics and Fine Processing School of Materials Science and Engineering Tsinghua University Beijing100084 China College of Physics and Optoelectronic Engineering Shenzhen University Guangdong518060 China Micro and Nano-Technology Program School of Natural and Applied Sciences Middle East Technical University Ankara06800 Turkey Department of Electrical and Computer Engineering University of Delaware NewarkDE19711 United States Department of Physics Middle East Technical University Ankara06800 Turkey

Mechanical straining-induced bandgap modulation in two-dimensional (2D) materials has been confined to volatile and narrow modulation due to substrate slippage and poor strain transfer. We report the thermomechanical modulation of the inherent bandgap in quasi-2D tellurium nanoflakes (TeNFs) via non-volatile strain induction during hot-press synthesis. We leveraged the coefficient of thermal expansion (CTE) mismatch between TeNFs and growth substrates by maintaining a high-pressure enforced non-slip condition during thermal relaxation (623 to 300K) to achieve the optimal biaxial compressive strain of -4.6 percent in TeNFs/sapphire. This resulted in an enormous bandgap modulation of 2.3 eV, at a rate of up to ~600 meV/%, which is two-fold larger than reported modulation rate. Strained TeNFs display robust band-to-band radiative excitonic blue photoemission with an intrinsic quantum efficiency (IQE) of c.a. 79.9%, making it promising for energy efficient blue LEDs and nanolasers. Computational studies reveal that biaxial compressive strain inhibits exciton-exciton annihilation by evading van-Hove singularities, hence promoting radiative-recombination. Bandgap modulation by such nonvolatile straining is scalable to other 2D semiconductors for on-demand nano(opto)-electronics. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy gap

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