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Association Between Long‑Term Exposure to Air Pollution and the Rate of Mortality After Hip Fracture Surgery in Patients Older Than 60 Years: Nationwide Cohort Study in Taiwan

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JMIR Public Health and Surveillance 2024年 10卷 e46591页

作者： Chuang, Shu-Han Kuo, Yi-Jie Huang, Shu-Wei Zhang, Han-Wei Peng, Hsiao-Ching Chen, Yu-Pin Division of General Practice Department of Medical Education Changhua Christian Hospital Changhua Taiwan Department of Orthopedics Wan Fang Hospital Taipei Medical University Taipei City Taiwan Department of Orthopedics School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Department of Applied Science National Taitung University Taitung County Taitung City Taiwan MetaTrial Research Center Biomedica Corporation New Taipei Taiwan Program for Aging China Medical University Taichung Taiwan Institute of Population Health Sciences National Health Research Institutes Miaoli Taiwan Department of Electrical and Computer Engineering Institute of Electrical Control Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

Background: To enhance postoperative patient survival, particularly in older adults, understanding the predictors of mortality following hip fracture becomes paramount. Air pollution, a prominent global environmental issue, has been linked to heightened morbidity and mortality across a spectrum of diseases. Nevertheless, the precise impact of air pollution on hip fracture outcomes remains elusive. Objective: This retrospective study aims to comprehensively investigate the profound influence of a decade-long exposure to 12 diverse air pollutants on the risk of post–hip fracture mortality among older Taiwanese patients (older than 60 years). We hypothesized that enduring long-term exposure to air pollution would significantly elevate the 1-year mortality rate following hip fracture surgery. Methods: From Taiwan’s National Health Insurance Research Database, we obtained the data of patients who underwent hip fracture surgery between July 1, 2003, and December 31, 2013. Using patients’ insurance registration data, we estimated their cumulative exposure levels to sulfur dioxide (SO2), carbon dioxide (CO2), carbon monoxide (CO), ozone (O3), particulate matter having a size of <10 μm (PM10), particulate matter having a size of <2.5 μm (PM2.5), nitrogen oxides (NOX), nitrogen monoxide (NO), nitrogen dioxide (NO2), total hydrocarbons (THC), nonmethane hydrocarbons (NMHC), and methane (CH4). We quantified the dose-response relationship between these air pollutants and the risk of mortality by calculating hazard ratios associated with a 1 SD increase in exposure levels over a decade. Results: Long-term exposure to SO2, CO, PM10, PM2.5, NOX, NO, NO2, THC, NMHC, and CH4 demonstrated significant associations with heightened all-cause mortality risk within 1 year post hip fracture surgery among older adults. For older adults, eac

关键词： air pollution environmental hazard exposure hazard hip fracture morbidity mortality older adult

A Long Short-term Memory Based Recurrent Neural Network for Interventional MRI Reconstruction

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arXiv 2022年

作者： Zhao, Ruiyang He, Zhao Wang, Tao Qiu, Suhao Herman, Pawel Hu, Yanle Zhang, Chencheng Shen, Dinggang Sun, Bomin Yang, Guang-Zhong Feng, Yuan School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign UrbanaIL United States Department of Neurosurgery Center for Functional Neurosurgery Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Division of computational science and technology KTH Royal Institute of Technology Stockholm Sweden Department of Radiation Oncology Mayo Clinic in Arizona PhoenixAZ United States School of Biomedical Engineering ShanghaiTech University Shanghai201210 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China

Interventional magnetic resonance imaging (i-MRI) for surgical guidance could help visualize the interventional process such as deep brain stimulation (DBS), improving the surgery performance and patient outcome. Different from retrospective reconstruction in conventional dynamic imaging, i-MRI for DBS has to acquire and reconstruct the interventional images sequentially online. Here we proposed a convolutional long short-term memory (Conv-LSTM) based recurrent neural network (RNN), or ConvLR, to reconstruct interventional images with golden-angle radial sampling. By using an initializer and Conv-LSTM blocks, the priors from the preoperative reference image and intra-operative frames were exploited for reconstructing the current frame. Data consistency for radial sampling was implemented by a soft-projection method. To improve the reconstruction accuracy, an adversarial learning strategy was adopted. A set of interventional images based on the pre-operative and postoperative MR images were simulated for algorithm validation. Results showed with only 10 radial spokes, ConvLR provided the best performance compared with state-of-the-art methods, giving an acceleration up to 40 folds. The proposed algorithm has the potential to achieve real-time i-MRI for DBS and can be used for general purpose MR-guided intervention. Copyright © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

Multimode Ultrastrong Coupling in Three-Dimensional Photonic-Crystal Cavities

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arXiv 2023年

作者： Tay, Fuyang Mojibpour, Ali Sanders, Stephen Liang, Shuang Xu, Hongjing Gardner, Geoff C. Baydin, Andrey Manfra, Michael J. Alabastri, Alessandro Hagenmüller, David Kono, Junichiro Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley–Curl Institute Rice University HoustonTX77005 United States Department of Physics and Astronomy Purdue University West LafayetteIN47907 United States Birck Nanotechnology Center Purdue University West LafayetteIN47907 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Smalley–Curl Institute Rice University HoustonTX77005 United States School of Materials Engineering Purdue University West LafayetteIN47907 United States Université de Strasbourg CNRS Strasbourg67000 France Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States

Recent theoretical studies have highlighted the role of spatially varying cavity electromagnetic fields in exploring novel cavity quantum electrodynamics (cQED) phenomena, such as the potential realization of the elusive Dicke superradiant phase transition. One-dimensional photonic-crystal cavities (PCCs), widely used for studying solid-state cQED systems, have uniform spatial profiles in the lateral plane. Three-dimensional (3D) PCCs, which exhibit discrete in-plane translational symmetry, overcome this limitation, but fabrication challenges have hindered the achievement of strong coupling in 3D-PCCs. Here, we report the realization of multimode ultrastrong coupling in a 3D-PCC at terahertz frequencies. The multimode coupling between the 3D-PCC’s cavity modes and the cyclotron resonance of a Landau-quantized two-dimensional electron gas in GaAs is significantly influenced by the spatial profiles of the cavity modes, leading to distinct coupling scenarios depending on the probe polarization. Our experimental results are in excellent agreement with a multimode extended Hopfield model that accounts for the spatial inhomogeneity of the cavity field. Guided by the model, we discuss the possible strong ground-state correlations between different cavity modes and introduce relevant figures of merit for the multimode ultrastrong coupling regime. Our findings emphasize the importance of spatially nonuniform cavity mode profiles in probing nonintuitive quantum phenomena expected for the ground states of cQED systems in the ultrastrong coupling regime. Copyright © 2023, The Authors. All rights reserved.

关键词： Electron cyclotron resonance

Photonic Metacrystal: Design and Experimental Results

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Photonic Metacrystal: Design and Experimental Results

Conference on Lasers and Electro-Optics (CLEO)

作者： S. Hu M. Khater E. Kratschmer S. Engelmann K. P. Arnold W. M. J. Green S. M. Weiss Department of Electrical and Computer Engineering Vanderbilt University Nashville Tennessee USA IBM T J Watson Center Yorktown Heights New York USA INanoBio Inc Menlo Park CA USA Interdisciplinary Materials Science Program Vanderbilt University Nashville Tennessee USA

Going beyond the limited design freedoms of traditional photonic crystals, we experimentally show how photonic metacrystals exploit the inclusion of subwavelength dielectric scatterers in the unit cell to deterministi... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Band structures Optical design Photonic crystals Aerospace electronics Laser modes Metamaterials Dielectrics

A simulated experiment to explore robotic dialogue strategies for people with dementia

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arXiv 2021年

作者： Yuan, Fengpei Sadovnik, Amir Zhang, Ran Casenhiser, Devin Paek, Eun Jin Yoon, Si On Zhao, Xiaopeng Department of Mechanical Aerospace and Biomedical Engineering University of Tennessee KnoxvilleTN United States Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN United States Department of Electrical and Computer Engineering Miami University OxfordOH United States Department of Audiology and Speech Pathology University of Tennessee Health Science Center KnoxvilleTN United States Department of Communication Sciences and Disorders University of Iowa Iowa CityIA United States

— People with Alzheimer’s disease and related dementias (ADRD) often show the problem of repetitive questioning, which brings a great burden on persons with ADRD (PwDs) and their caregivers. Conversational robots hold promise of coping with this problem and hence alleviating the burdens on caregivers. In this paper, we proposed a partially observable markov decision process (POMDP) model for the PwD-robot interaction in the context of repetitive questioning, and used Q-learning to learn an adaptive conversation strategy (i.e., rate of follow-up question and difficulty of follow-up question) towards PwDs with different cognitive capabilities and different engagement levels. The results indicated that Q-learning was helpful for action selection for the robot. This may be a useful step towards the application of conversational social robots to cope with repetitive questioning in PwDs. Copyright © 2021, The Authors. All rights reserved.

关键词： Reinforcement learning

Optimal First-Order Algorithms as a Function of Inequalities

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arXiv 2021年

作者： Park, Chanwoo Ryu, Ernest K. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology MA United States Department of Mathematical Sciences Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Republic of

In this work, we present a novel algorithm design methodology that finds the optimal algorithm as a function of inequalities. Specifically, we restrict convergence analyses of algorithms to use a prespecified subset of inequalities, rather than utilizing all true inequalities, and find the optimal algorithm subject to this restriction. This methodology allows us to design algorithms with certain desired characteristics. As concrete demonstrations of this methodology, we find new state-of-the-art accelerated first-order gradient methods using randomized coordinate updates and backtracking line searches. © 2021, CC BY-NC-ND.

关键词： Gradient methods

Scaling Limitations of Line TFETs at Sub-8-nm Technology Node

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Scaling Limitations of Line TFETs at Sub-8-nm Technology Nod...

2020 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2020

作者： Thoti, Narasimhulu Li, Yiming Kola, Sekhar Reddy Parallel and Scientific Computing Laboratory Eecs International Graduate Program Institute of Communications Engineering Department of Electrical and Computer Engineering Center for MmWave Smart Radar System and Technologies National Chiao Tung University 1001 Ta-Hsueh Rd Hsinchu300 Taiwan

ISBN: (纸本)9781728142326

The scope of the work is to investigate limitations in device scaling by identifying various parameters of short channel effects (SCEs) in current challenging geometries of the line tunnel field effect transistors (TFETs). Key factors of the considered device, such as the doping and the thickness (tn) of n-epitaxial region, and source-to-drain length (LSDeff) scaling cannot be tuned anymore to boost the device characteristics for the sub-8-nm technology node. The main results of this study indicates that the engineering acceptable performance are achieved at a low doping of 5 1018 cm-3, an optimal tn as low (about 0.5 nm), and a LSDeff greater than 12.5 nm. Hence, the line TFETs below sub-8-nm faces serious bottleneck of scaling and cannot be further scaled with the conventional scaling rule at all. © 2020 IEEE.

关键词： Tunnel field effect transistors

Accelerating neural architecture search via proxy data

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arXiv 2021年

作者： Na, Byunggook Mok, Jisoo Choe, Hyeokjun Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of AIIS ASRI INMC Interdisciplinary Program in AI Seoul National University Seoul Korea Republic of

Despite the increasing interest in neural architecture search (NAS), the significant computational cost of NAS is a hindrance to researchers. Hence, we propose to reduce the cost of NAS using proxy data, i.e., a representative subset of the target data, without sacrificing search performance. Even though data selection has been used across various fields, our evaluation of existing selection methods for NAS algorithms offered by NAS-Bench-1shot1 reveals that they are not always appropriate for NAS and a new selection method is necessary. By analyzing proxy data constructed using various selection methods through data entropy, we propose a novel proxy data selection method tailored for NAS. To empirically demonstrate the effectiveness, we conduct thorough experiments across diverse datasets, search spaces, and NAS algorithms. Consequently, NAS algorithms with the proposed selection discover architectures that are competitive with those obtained using the entire dataset. It significantly reduces the search cost: executing DARTS with the proposed selection requires only 40 minutes on CIFAR-10 and 7.5 hours on ImageNet with a single GPU. Additionally, when the architecture searched on ImageNet using the proposed selection is inversely transferred to CIFAR-10, a state-of-the-art test error of 2.4% is yielded. Our code is available at https://***/nabk89/NAS-with-Proxy-data. © 2021, CC BY-NC-ND.

关键词： Data reduction

Recurrence plots and convolutional neural networks applied to nonintrusive load monitoring

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Recurrence plots and convolutional neural networks applied t...

2020 IEEE Power and Energy Society General Meeting, PESGM 2020

作者： Cavalca, Diego L. Fernandes, Ricardo A. S. Federal University of São Carlos Graduate Program in Computer Science São Carlos Brazil Federal University of São Carlos Electrical Engineering Department São Carlos Brazil

ISBN: (纸本)9781728155081

The need to increasingly promote the efficient use of electricity for residential consumers motivated some researches to obtain solutions that allow the identification of loads for use in demand response programs and in the energy consumption disaggregation. Recently, the use of Convolutional Neural Networks for load identification has been common. However, it is necessary to transform the temporal signal into an image. In this sense, the present paper proposes a novel feature engineering stage, based on Recurrence Plots, to obtain the images for Nonintrusive Load Monitoring. In order to prove its effectiveness, the Recurrence Plots were compared to the Gramian Angular Difference Fields, which had already been applied in this context. For this purpose, it was used the REDD dataset, in which only the low frequency data measured in the main distribution panel were considered. Individual load measurements were only used for the labelling of the 90-seconds windows. The Convolutional Neural Network was trained and validated, considering two different strategies (multi-label and binary models), obtaining an F1-score of 66.05% and 72.65%, respectively. © 2020 IEEE.

关键词： Machine learning