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Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients:A Multi-Center Retrospective Study in China

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engineering 2022年第1期8卷 116-121页

作者： Ye Yuan Chuan Sun Xiuchuan Tang Cheng Cheng Laurent Mombaerts Maolin Wang Tao Hu Chenyu Sun Yuqi Guo Xiuting Li Hui Xu Tongxin Ren Yang Xiao Yaru Xiao Hongling Zhu Honghan Wu Kezhi Li Chuming Chen Yingxia Liu Zhichao Liang Zhiguo Cao Hai-Tao Zhang Ioannis Ch.Paschaldis Quanying Liu Jorge Goncalves Qiang Zhong Li Yan School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China Luxembourg Centre for Systems Biomedicine University of LuxembourgBelval L-4367Luxembourg Department of Plant Sciences University of CambridgeCambridge CB21TNUK Department of Emergency Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China AMITA Health Saint Joseph Hospital Chicago ChicagoIL 60657USA Department of Anesthesiology Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Huazhong University of Science and Technology-Wuxi Research Institute Wuxi 214174China Division of Cardiology Department of Internal MedicineTongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Institute of Health Informatics University College LondonLondon NW12DAUK Department of Biomedical Engineering Southern University of Science and TechnologyShenzhen 518055China Department of Infectious Diseases Shenzhen Key Laboratory of Pathogenic Microbiology and ImmunologyNational Clinical Research Center for Infectious DiseaseThe Third People’s Hospital of Shenzhen(Second Hospital Affiliated with the Southern University of Science and Technology)Shenzhen 518055China Department of Electrical and Computer Engineering&Division of Systems Engineering&Department of Biomedical Engineering Boston UniversityBostonMA 02215USA

Coronavirus disease 2019(COVID-19)has become a worldwide *** patients of COVID-19 suffer from a high mortality rate,motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk ***,we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital,Wuhan,China(development cohort)and externally validated with data from two other centers:141 inpatients from Jinyintan Hospital,Wuhan,China(validation cohort 1)and 432 inpatients from The Third People’s Hospital of Shenzhen,Shenzhen,China(validation cohort 2).The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of *** risk score can predict the mortality of individual patients more than 12 d in advance with more than 90%accuracy across all ***,the Kaplan-Meier score shows that patients can be clearly differentiated upon admission as low,intermediate,or high risk,with an area under the curve(AUC)score of *** summary,a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2(SARS-CoV-2);it has also been validated in independent cohorts.

关键词： COVID-19 Risk score Mortality risk prediction

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Hardware implementation of memristor-based artificial neural networks

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Nature Communications 2024年第1期15卷 1974页

作者： Aguirre, Fernando Sebastian, Abu Le Gallo, Manuel Song, Wenhao Wang, Tong Yang, J. Joshua Lu, Wei Chang, Meng-Fan Ielmini, Daniele Yang, Yuchao Mehonic, Adnan Kenyon, Anthony Villena, Marco A. Roldán, Juan B. Wu, Yuting Hsu, Hung-Hsi Raghavan, Nagarajan Suñé, Jordi Miranda, Enrique Eltawil, Ahmed Setti, Gianluca Smagulova, Kamilya Salama, Khaled N. Krestinskaya, Olga Yan, Xiaobing Ang, Kah-Wee Jain, Samarth Li, Sifan Alharbi, Osamah Pazos, Sebastian Lanza, Mario Physical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Departament d’Enginyeria Electrònica Universitat Autònoma de Barcelona (UAB) Barcelona 08193 Spain IBM Research – Zurich Rüschlikon Switzerland Department of Electrical and Computer Engineering University of Southern California (USC) Los Angeles 90089 CA United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor 48109 MI United States Department of Electrical Engineering National Tsing Hua University Hsinchu 30013 Taiwan Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano and IUNET Piazza L. da Vinci 32 Milano 20133 Italy School of Electronic and Computer Engineering Peking University Shenzhen China Department of Electronic and Electrical Engineering University College London (UCL) Torrington Place WC1E 7JE London United Kingdom Departamento de Electrónica y Tecnología de Computadores Facultad de Ciencias Universidad de Granada Avenida Fuentenueva s/n Granada 18071 Spain Engineering Product Development (EPD) Pillar Singapore University of Technology & amp Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province Hebei University Baoding 071002 China Department of Electrical and Computer Engineering College of Design and Engineering National University of Singapore (NUS) Singapore Singapore

Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach. © The Author(s) 2024.

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Thick Film Interdigitated Electrode Graphite and Carbon Nanotube on Flexible Substrate for Hydrogen Gas Detection

Thick Film Interdigitated Electrode Graphite and Carbon Nano...

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IEEE International Conference on Sensors and Nanotechnology

作者： Azlinda Abu Bakar Mohd Nizar Hamidon Mohd Hanif Yaacob Suriati Paiman Haslina Jaafar Wan Nursheila Wan Jusoh Funtional Device Laboratory Institute of Nanoscience and Nanotechnology Universiti Putra Malaysia Serdang Selangor Department of Computer and Communication Systems Faculty of Engineering Universiti Putra Malaysia Serdang Selangor Department of Physics Faculty of Science Universiti Putra Malaysia Serdang Selangor Department of Electrical and Electronic Faculty of Engineering Universiti Putra Malaysia Serdang Selangor Section of Aerospace Malaysians Institute of Aviation Technology Universiti Kuala Lumpur Dengkil Selangor

The comparison on the performance of interdigitated electrode (IDE) graphite and carbon nanotube (CNT) using titanium dioxide-multiwall carbon nanotube (TiO2-MWCNT) composite as sensing materials to detect hydrogen gas is presented in this research work. Screen printing technique has been selected to deposited IDE graphite and CNT conductive pastes as the electrode thick film on a flexible substrate, which is continued by firing at 120°C each. TiO 2 -MWCNT paste was printed as a sensing layer on top of IDE, followed by firing at 350°C. FESEM was used to characterize the morphological analysis of the IDE film. The operating temperature was varied at 27°C, 50°C and 100°C and concentration varied from 100–1000 ppm for gas hydrogen. The gas sensor demonstrated as n-type gas characteristics where the response of the gas sensor produced increased current from the results. The results purpose when the sensor was exposed to hydrogen gas. Therefore, the ability of IDE graphite and CNT as an electrode to harvest the electron from sensing materials were successfully proven. The IDE CNT showed better response compare to IDE graphite, but both electrodes were able to sense 100 – 1000 ppm at the operating temperature of 27°C, 50°C and 100°C. The optimal of operating temperature for IDE graphite is 50°C and IDE CNT is at room temperature for the hydrogen gas detections in this research work.

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Optical data transmission using inverse-designed silicon multimode photonic circuits and spectrally flattened microcombs

Optical data transmission using inverse-designed silicon mul...

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Conference on Lasers and Electro-Optics (CLEO)

作者： K. Y. Yang C. Shirpurkar A. D. White J. Zang L. Chang F. Ashtiani M. A. Guidry D. M. Lukin S. V. Pericherla J. Yang H. Kwon J. Lu G. H. Ahn K. Van Gasse Y. Jin S. -P. Yu T. C. Briles J. R. Stone D. R. Carlson H. Song K. Zou H. Zhou K. Pang H. Hao L. Trask M. Li A. Netherton L. Rechtman J. S. Stone J. L. Skarda L. Su D. Vercruysse J. P. W. MacLean S. Aghaeimeibodi M.-J. Li D. A.B. Miller D. M. Marom A. E. Willner J. E. Bowers S. B. Papp P. J. Delfyett F. Aflatouni J. Vučković E.L. Ginzton Laboratory Stanford University Stanford CA USA John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge MA USA The College of Optics and Photonics University of Central Florida Orlando FL USA Time and Frequency Division National Institute of Standards and Technology Boulder CO USA Department of Physics University of Colorado Boulder CO USA Department of Electrical and Computer Engineering University of California Santa Barbara CA USA Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia PA USA SPINS Photonics Incorporated Hollister CA USA Octave Photonics Louisville CO USA Department of Electrical and Computer Engineering University of Southern California Los Angeles CA USA Applied Physics Department The Hebrew University of Jerusalem Jerusalem Israel Corning Incorporated Corning NY USA

We demonstrate a 1.12-Tbps error-free data transmission throughout inverse-designed multimode photonic circuits using spectrally flattened microcombs as a multi-wavelength laser source.

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An Integer Clustering Approach for Modeling Large-Scale EV Fleets with Guaranteed Performance

arXiv

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arXiv 2023年

作者： Geng, Sijia Lee, Thomas Mallapragada, Dharik Botterud, Audun The Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States The MIT Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA02139 United States The MIT Energy Initiative Massachusetts Institute of Technology CambridgeMA02139 United States The MIT Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States

Large-scale integration of electric vehicles (EVs) leads to a tighter integration between transportation and electric energy systems. In this paper, we develop a novel integer-clustering approach to model a large number of EVs that manages vehicle charging and energy at the fleet level yet maintain individual trip dispatch. The model is then used to develop a spatially and temporally-resolved decision-making tool for optimally planning and/or operating EV fleets and charging infrastructure. The tool comprises a two-stage framework where a tractable disaggregation step follows the integer-clustering problem to recover an individually feasible solution. Mathematical relationships between the integer clustering, disaggregation, and individual formulations are analyzed. We establish theoretical lower and upper bounds on the true individual formulation which underpins a guaranteed performance of the proposed method. The optimality accuracy and computational efficiency of the integer-clustering formulation are also numerically validated on a real-world case study of Boston's public transit network under extensive test instances. Substantial speedups with minimal loss in solution quality are demonstrated. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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PAM-4 Optical Transmission Beyond 224 Gbps Based on an Ultrahigh-Bandwidth Slow-light Silicon Modulator

PAM-4 Optical Transmission Beyond 224 Gbps Based on an Ultra...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Changhao Han Jun Qin Qipeng Yang Zhao Zheng Haowen Shu Yunhao Zhang Yichen Wu Yu Sun Junde Lu Yan Zhou Zhangfeng Ge Lei Wang Zhixue He Shaohua Yu Weiwei Hu Chao Peng John E. Bowers Xingjun Wang State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of California Santa Barbara CA US Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing China Frontiers Science Center for Nano-optoelectronics Peking University Beijing China Peng Cheng Laboratory Shenzhen China Peking University Yangtze Delta Institute of Optoelectronics Nantong China

We experimentally demonstrate PAM-4 optical transmission beyond 224 Gbps based on an ultrahigh-bandwidth slow-light silicon modulator in C-band with the combination of the artificial neural network equalizers.

ISBN: (纸本)9798350369311

关键词： Equalizers C-band Optical computing Artificial neural networks Electro-optic modulators Optical fiber networks Silicon photonics Ultrafast optics Optical modulation Passband

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EFaR 2023: Efficient Face Recognition Competition

EFaR 2023: Efficient Face Recognition Competition

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Jan Niklas Kolf Fadi Boutros Jurek Elliesen Markus Theuerkauf Naser Damer Mohamad Alansari Oussama Abdul Hay Sara Alansari Sajid Javed Naoufel Werghi Klemen Grm Vitomir Štruc Fernando Alonso-Fernandez Kevin Hernandez Diaz Josef Bigun Anjith George Christophe Ecabert Hatef Otroshi Shahreza Ketan Kotwal Sébastien Marcel Iurii Medvedev Bo Jin Diogo Nunes Ahmad Hassanpour Pankaj Khatiwada Aafan Ahmad Toor Bian Yang Fraunhofer Institute for Computer Graphics Research IGD Germany TU Darmstadt Germany Department of Electrical and Computer Engineering Khalifa University Abu Dhabi United Arab Emirates Laboratory for Machine Intelligence Faculty of Electrical Engineering University of Ljubljana Slovenia Halmstad University Sweden Idiap Research Institute Martigny Switzerland École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Université de Lausanne (UNIL) Lausanne Switzerland Institute of Systems and Robotics University of Coimbra Coimbra Portugal Department of Information Security and Communication Technology eHealth and Welfare Security Group Norwegian University of Science and Technology Norway

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well.

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Edge Learning for Large-Scale Internet of Things With Task-Oriented Efficient Communication

arXiv

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arXiv 2023年

作者： Xie, Haihui Xia, Minghua Wu, Peiran Wang, Shuai Poor, H. Vincent The School of Electronics and Information Technology Sun Yat-Sen University Guangzhou510006 China The Southern Marine Science and Engineering Guangdong Laboratory Zhuhai519082 China The Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

In the Internet of Things (IoT) networks, edge learning for data-driven tasks provides intelligent applications and services. As the network size becomes large, different users may generate distinct datasets. Thus, to suit multiple edge learning tasks for large-scale IoT networks, this paper performs efficient communication under the task-oriented principle by using the collaborative design of wireless resource allocation and edge learning error prediction. In particular, we start with multi-user scheduling to alleviate co-channel interference in dense networks. Then, we perform optimal power allocation in parallel for different learning tasks. Thanks to the high parallelization of the designed algorithm, extensive experimental results corroborate that the multi-user scheduling and task-oriented power allocation improve the performance of distinct edge learning tasks efficiently compared with the state-of-the-art benchmark algorithms. © 2023, CC BY-NC-SA.

关键词： Internet of things

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Interval type-2 enhanced possibilistic fuzzy c-means clustering for gene expression data analysis

arXiv

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arXiv 2021年

作者： Sotudian, Shahabeddin Zarandi, Mohammad Hossein Fazel Department of Electrical and Computer Engineering Division of Systems Engineering Boston University Boston United States Knowledge/Intelligent Systems Laboratory Department of Mechanical and Industrial Engineering University of Toronto Toronto Canada Department of Industrial Engineering Amirkabir University of Technology Tehran Iran

Both FCM and PCM clustering methods have been widely applied to pattern recognition and data clustering. Nevertheless, FCM is sensitive to noise and PCM occasionally generates coincident clusters. PFCM is an extension of the PCM model by combining FCM and PCM, but this method still suffers from the weaknesses of PCM and FCM. In the current paper, the weaknesses of the PFCM algorithm are corrected and the enhanced possibilistic fuzzy c-means (EPFCM) clustering algorithm is presented. EPFCM can still be sensitive to noise. Therefore, we propose an interval type-2 enhanced possibilistic fuzzy c-means (IT2EPFCM) clustering method by utilizing two fuzzifiers (m1, m2)for fuzzy memberships and two fuzzifiers (θ1, θ2)for possibilistic typicalities. Our computational results show the superiority of the proposed approaches compared with several state-of-the-art techniques in the literature. Finally, the proposed methods are implemented for analyzing microarray gene expression data. © 2021, CC BY-NC-ND.

关键词： Cluster analysis

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Stretchable triboelectric sensor for measurement of the forearm muscles movements and fingers motion for Parkinson's disease assessment and assisting technologies

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Medical Devices and Sensors 2021年第1期4卷

作者： Vera Anaya, David Yuce, Mehmet Rasit Department of Electrical and Computer Systems Engineering Monash University ClaytonVIC Australia Biomedical Integrated Circuits and Sensors Laboratory Monash University ClaytonVIC Australia

Parkinson's disease (PD) is a degenerative, neurological disease that causes motor dysfunctions on the patient. Currently, the symptoms and progression of the disease are assessed by physicians who diagnose through careful observation, according to the Movement Disorder Society (MDS)-sponsored Revision of the Unified Parkinson's Disease Rating Scale (UPDRS). In this manuscript, we report a portable sensor device based on a flexible Triboelectric Nanogenerator (TENG) formed from a dielectric material with a size of 4 cm × 3.5 cm and an aluminium (Al) electrode with dimensions of 2 mm × 3 mm, to monitor the movement of forearm muscles and tendons. When the hand or fingers extend or flex, the short gap between the dielectric polymer and the small conductor changes, generating a voltage due to triboelectric contact. The device is used to assess localized bradykinesia and rigidity of fingers and hands, as well as wrist and arm tremors. The voltage signal of the proposed sensor shows different waveform shapes, time duration and amplitude for each level of classification from ‘normal’ to ‘moderate’ status, described on the MDS-UPDRS examination of hand movement, finger tapping and pronation–supination movement. Hence, we demonstrated that the proposed sensor can potentially be used for quantification of the symptoms and diagnosing. In addition, single finger motion tracking is also achieved by placing the triboelectric sensor in different positions around the forearm, close to the tendon connected to the finger under test. This way we avoid the placement of the sensor directly on the fingers, improving comfortability and long-term wearability. The presented TENG sensor is fully integrated with the processing and the transmission board exploring new opportunities for triboelectric transducers in small motion detection of muscles. The future aim of this research is to provide real-time monitoring of the disease progression as feedback for personalized treatment of the PD pat

关键词： Nanogenerators

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