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Electron-induced chemistry and sputtering of volatile species from amorphous and crystalline water ice

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arXiv 2025年

作者： Mishra, Yogeshwar Nath Chandra, Sankhabrata Gudipati, Murthy S. Henderson, Bryana Hanstorp, Dag Yung, Yuk L. Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Department of Physics University of Gothenburg GothenburgSE 41296 Sweden Division of Geological and Planetary Science California Institute of Technology PasadenaCA91125 United States Computer Electrical and Mathematical Sciences and Engineering Division KAUST Saudi Arabia

Electron irradiation of water-rich ices plays a significant role in initiating the chemical and physical processes on the surface of airless icy bodies in radiation environments such as Europa and Enceladus, as well as on the Moon, comets, and asteroids interacting with the solar wind. The sputtering process by electrons and ions leads to chemical modification and outgassing of their icy surfaces and the subsequent formation of a tenuous atmosphere. Though electron-sputtering yields are known to be lower compared to ion-sputtering yields, one needs to also account for their differential fluxes. Europa receives about 10 times more electron flux than proton flux, making electron-induced outgassing important to study and understand. Here, we report a rigorous experimental study of electron bombardment of Europa analog crystalline and amorphous pure water ices at 100 K under ultrahigh vacuum and compare our results with reported data in the literature to provide quantitative data for low-energy electron-induced chemistry and sputtering of water-ice. In our experiments, the electron-induced sputtering yields of all the gaseous species H2, O, OH, H2O, and O2 are investigated for electron energies lower than 2 keV in terms of partial pressure vs. time of irradiation. The effective averaged change in partial pressure of the desorbed species is converted to the number of sputtered atoms or molecules per second per cm2 from the ice, and then to the sputtering yields (number of species sputtered per electron). These values at different electron fluences (electrons/cm2) are then used to derive the number of atoms or molecules desorbed or sputtered per keV of incident electrons, to enable utilizing our data to quantify the contribution of electron-sputtering process towards the exospheric composition of Europa. Our data agrees well with the previously reported data for the sputtering of O2 and H2O yield for the amorphous ice. We also find that crystalline ice shows significantly

关键词： Electron irradiation

Screening cognitive impairment in patients with atrial fibrillation: A deep learning model based on retinal fundus photographs

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Heart Rhythm O2 2025年第5期6卷 678-686页

作者： Wang, Zhen Li, Mingxiao Xia, Peng Jiang, Chao Shen, Ting Ma, Jiaming Bai, Yu Zhang, Suhui Lai, Yiwei Li, Sitong Xu, Hui Xu, Yang Ma, Tong Ju, Lie He, Liu Dong, Li Sang, Caihua Long, Deyong Chen, Yuzhong Du, Xin Ge, Zongyuan Dong, Jianzeng Wei, Wen-Bin Ma, Changsheng Department of Cardiology Beijing Anzhen Hospital Capital Medical University Beijing China Engineering Research Center of Medical Devices for Cardiovascular Diseases Ministry of Education Capital Medical University Beijing China National Clinical Research Center for Cardiovascular Diseases Beijing China Beijing Airdoc Technology Co. Ltd. Beijing China Department of Electrical and Computer Systems Engineering Faculty of Engineering Monash University Clayton Australia Yale School of Medicine New Haven Connecticut United States School of Clinical Medicine Peking Union Medical College and Chinese Academy of Medical Science Beijing China Beijing Tongren Eye Center Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment Beijing Ophthalmology & Visual Sciences Key Lab Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology Beijing Tongren Hospital Capital Medical University Beijing China Monash e-Research Center Monash University Clayton Australia

Background: Patients with atrial fibrillation (AF) have a higher risk of cognitive impairment (CI). However, complexity of CI diagnosis and lack of simple screening approaches limited early screening and intervention of CI in AF patients. Objective: Our study aimed to develop deep learning models based on fundus photographs for easy screening of CI in AF patients. Methods: From May 2021 to April 2023, patients who completed fundus examination and cognitive function evaluation in the Chinese Atrial Fibrillation Registry Study were included. The training and validation sets were randomly split at an 8:2 ratio. Participants from the Beijing Eye Study served as the external validation set. Different deep learning models were trained, and their CI detection ability was validated. Results: A total of 899 patients in the Chinese Atrial Fibrillation Registry Study were included. In the validation set, the vision-ensemble model based on fundus images alone had an area under the receiver-operating characteristic curve (AUROC) of 0.855 (95% confidence interval 0.816–0.894) for CI screening. The multimodal model (AUROC 0.861, 95% confidence interval 0.823–0.898), based on fundus photographs and 4 clinical variables, performed comparably to the vision-ensemble model. The AUROC of the vision-ensemble model for CI screening achieved 0.773 (95% confidence interval 0.709–0.837) in the external test set. In the saliency map, the vision-ensemble model focused on areas around retinal vessels and the optic disc. Conclusion: A vision-ensemble model based on fundus images might be practical for preliminary screening of CI in AF patients. © 2025 Heart Rhythm Society

关键词： Artificial intelligence Atrial fibrillation Cognitive impairment Deep learning Fundus photography

Giant Thermomechanical Bandgap engineering in Quasi-two-dimensional Tellurium

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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

Anisotropic subwavelength grating perturbation enables zero crosstalk in a leaky mode

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arXiv 2022年

作者： Kabir, Md Faiyaz Mia, Md Borhan Ahmed, Ishtiaque Jaidye, Nafiz Ahmed, Syed Z. Kim, Sangsik Department of Electrical and Computer Engineering Texas Tech University LubbockTX79409 United States Department of Physics and Astronomy Texas Tech University LubbockTX79409 United States School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of

Electromagnetic coupling via exponentially decaying evanescent field or radiative wave is a primary characteristic of light, allowing optical signal/power transfer but limiting integration density in a photonic circuit. A leaky mode combines both evanescent field and radiative wave, causing stronger coupling and thus not ideal for dense integration. Here we show that a leaky mode with anisotropic perturbation rather can achieve completely zero crosstalk realized by subwavelength grating (SWG) metamaterials. The oscillating fields in the SWGs enable coupling coefficients in each direction to counteract each other, resulting in completely zero crosstalk. We experimentally demonstrate such an extraordinarily low coupling between closely spaced identical leaky SWG waveguides, suppressing the crosstalk by ≈40 dB compared to conventional strip waveguides, corresponding to ≈100 times longer coupling length. This leaky-SWG suppresses the crosstalk of transverse–magnetic (TM) mode, which is challenging due to its low confinement, and marks a novel approach in electromagnetic coupling applicable to other spectral regimes and generic devices. Copyright © 2022, The Authors. All rights reserved.

关键词： Crosstalk

Compressed Channel Estimation for IRS-Assisted Millimeter Wave OFDM Systems: A Low-Rank Tensor Decomposition-Based Approach

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arXiv 2022年

作者： Zheng, Xi Wang, Peilan Fang, Jun Li, Hongbin The National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu611731 China The Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States

We consider the problem of downlink channel estimation for intelligent reflecting surface (IRS)-assisted millimeter Wave (mmWave) orthogonal frequency division multiplexing (OFDM) systems. By exploring the inherent sparse scattering characteristics of mmWave channels, we show that the received signals can be expressed as a low-rank third-order tensor that admits a tensor rank decomposition, also known as canonical polyadic decomposition (CPD). A structured CPD-based method is then developed to estimate the channel parameters. Our analysis reveals that the training overhead required by our proposed method is as low as O(U2), where U denotes the sparsity of the cascade channel. Simulation results are provided to illustrate the efficiency of the proposed method. Copyright © 2022, The Authors. All rights reserved.

关键词： Millimeter waves

Enhanced Labeling Technique for Reddit Text and Fine-Tuned Longformer Models for Classifying Depression Severity in English and Luganda

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Enhanced Labeling Technique for Reddit Text and Fine-Tuned L...

International Conference on Information and Communication Technology Convergence (ICTC)

作者： Richard Kimera Daniela N. Rim Joseph Kirabira Ubong Godwin Udomah Heeyoul Choi Department of Advanced Convergence Handong Global University Pohang South Korea School of Computer Science and Electrical Engineering Handong Global University Pohang South Korea Department of Psychiatry Busitema University (Faculty of Health Sciences) Mbale Uganda Department of Psychiatry University of Uyo Teaching Hospital Uyo Akwa Ibom Nigeria

Depression is a global burden and one of the most challenging mental health conditions to control. Using the BDI questionnaire, experts can detect its severity early, administer appropriate medication to patients, and impede its progression. Owing to the fear of potential stigmatization, many patients turn to social media platforms such as Reddit for advice and assistance at various stages of their journey. This research extracts text from Reddit to facilitate the diagnostic process, employs a proposed labelling approach to categorize the text, and subsequently fine-tunes the Longformer model. The model’s performance is compared against the baseline models, including Naive Bayes, Random Forest, Support Vector Machines, and Gradient Boosting. Our findings reveal that the Longformer model outperforms the baseline models in both English (48%) and Luganda (45%) languages on a custom-made dataset.

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Current and future directions in network biology

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arXiv 2023年

作者： Zitnik, Marinka Li, Michelle M. Wells, Aydin Glass, Kimberly Gysi, Deisy Morselli Krishnan, Arjun Murali, T.M. Radivojac, Predrag Roy, Sushmita Baudot, Anaïs Bozdag, Serdar Chen, Danny Z. Cowen, Lenore Devkota, Kapil Gitter, Anthony Gosline, Sara Gu, Pengfei Guzzi, Pietro H. Huang, Heng Jiang, Meng Kesimoglu, Ziynet Nesibe Koyuturk, Mehmet Ma, Jian Pico, Alexander R. Pržulj, Nataša Przytycka, Teresa M. Raphael, Benjamin J. Ritz, Anna Sharan, Roded Shen, Yang Singh, Mona Slonim, Donna K. Tong, Hanghang Yang, Xinan Holly Yoon, Byung-Jun Yu, Haiyuan Milenković, Tijana Department of Biomedical Informatics Harvard University United States Department of Computer Science and Engineering University of Notre Dame United States Lucy Family Institute for Data and Society University of Notre Dame United States Eck Institute for Global Health University of Notre Dame United States Channing Division of Network Medicine Brigham and Women’s Hospital Harvard Medical School United States Department of Statistics Federal University of Paraná Brazil Department of Physics Northeastern University United States Department of Biomedical Informatics University of Colorado United States Department of Computer Science Virginia Tech United States Khoury College of Computer Sciences Northeastern University United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison United States Wisconsin Institute for Discovery United States Aix Marseille Université INSERM MMG Marseille France Department of Computer Science and Engineering University of North Texas United States Department of Mathematics University of North Texas United States Department of Computer Science Tufts University United States Morgridge Institute for Research United States Pacific Northwest National Laboratory United States Department of Medical and Surgical Sciences University Magna Graecia of Catanzaro Italy Department of Computer Science University of Maryland College Park United States National Center of Biotechnology Information National Library of Medicine National Institutes of Health Department of Computer and Data Sciences Case Western Reserve University United States Computational Biology Department School of Computer Science Carnegie Mellon University United States Institute of Data Science and Biotechnology Gladstone Institutes Department of Computer Science University College London United Kingdom ICREA Catalan Institution for Research and Advanced Studies Spain Department of Computer Science Princeton University United States Departmen

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These challenges stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology and highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on the future directions of network biology. Additionally, we offer insights into scientific communities, educational initiatives, and the importance of fostering diversity within the field. This paper establishes a roadmap for an immediate and long-term vision for network biology. © 2023, CC BY.

关键词： Machine learning

Overview of Ancillary Services in the Nigerian Power Sector 2

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Overview of Ancillary Services in the Nigerian Power Sector

2nd International Conference on Multidisciplinary engineering and Applied science, ICMEAS 2023

作者： Saka, Bemdoo Hussein, Suleiman U. Ishola, Biliaminu A. Mohammed, Yekini S. Hembe, Nanen D. Mustapha, Umar M. Thomas, Sadiq Nile University of Nigeria Dept. of Electrical/Electronics Eng. Abuja Nigeria Centre for Satellite Tech. Development Nat. Space Research and Dev. Agency Abuja Nigeria National Control Center Transmission Company of Nigeria Osogbo Abuja Nigeria Federal Polytechnic Nasarawa Dept. of Electrical/Electronics Eng. Nasarawa Nigeria Nasarawa State University Department of Geography Nasarawa Nigeria Advanced UAV Laboratory Nat. Space Research and Dev. Agency Abuja Nigeria Nile University of Nigeria Dept. of Computer Engineering Abuja Nigeria

ISBN: (纸本)9798350358834

An overview of the ancillary services in the Nigerian power sector is provided in this work. These services are essential in the power system because they are additional services required by the grid during critical times to enable the power grid to operate in a stable, safe, and reliable manner. The ancillary services provided as stipulated in the Grid Code are black start, frequency control, operating reserves, and voltage control. The System Operator procures the relevant ancillary service depending on the requirement of the grid at a particular time. The paper further presents some ways of improving the viability of ancillary services for the energy grid of Nigeria. These include making incentives more attractive for suppliers of ancillary services, utilizing more hydroelectric power plants for generation and load balancing, integrating distributed generation to provide support to the reactive power, and making necessary investments in the grid. © 2023 IEEE.

关键词： Hydroelectric power plants

Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Framework

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arXiv 2023年

作者： Zheng, Jingheng Ni, Wanli Tian, Hui Gündüz, Deniz Quek, Tony Q.S. Han, Zhu The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Modena41100 Italy The Singapore University of Technology and Design 487372 Singapore The Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

Under the organization of the base station (BS), wireless federated learning (FL) enables collaborative model training among multiple devices. However, the BS is merely responsible for aggregating local updates during the training process, which incurs a waste of the computational resource at the BS. To tackle this issue, we propose a semi-federated learning (SemiFL) paradigm to leverage the computing capabilities of both the BS and devices for a hybrid implementation of centralized learning (CL) and FL. Specifically, each device sends both local gradients and data samples to the BS for training a shared global model. To improve communication efficiency over the same time-frequency resources, we integrate over-the-air computation for aggregation and non-orthogonal multiple access for transmission by designing a novel transceiver structure. To gain deep insights, we conduct convergence analysis by deriving a closed-form optimality gap for SemiFL and extend the result to two extra cases. In the first case, the BS uses all accumulated data samples to calculate the CL gradient, while a decreasing learning rate is adopted in the second case. Our analytical results capture the destructive effect of wireless communication and show that both FL and CL are special cases of SemiFL. Then, we formulate a non-convex problem to reduce the optimality gap by jointly optimizing the transmit power and receive beamformers. Accordingly, we propose a two-stage algorithm to solve this intractable problem, in which we provide the closed-form solutions to the beamformers. Extensive simulation results on two real-world datasets corroborate our theoretical analysis, and show that the proposed SemiFL outperforms conventional FL and achieves 3.2% accuracy gain on the MNIST dataset compared to state-of-the-art benchmarks. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency