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arXiv 2018年

作者： Causey, Jason Zhang, Junyu Ma, Shiqian Jiang, Bo Qualls, Jake Politte, David G. Prior, Fred Zhang, Shuzhong Huang, Xiuzhen Department of Computer Science Arkansas State University JonesboroAR72467 United States Department of Industrial and Systems Engineering University of Minnesota MinneapolisMN55455 United States Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Shatin N.T. Hong Kong Research Center for Management Science and Data Analytics School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai200433 China Mallinckrodt Institute of Radiology Washington University St. LouisMO63110 United States Department of Biomedical Informatics University of Arkansas for Medical Sciences Little RockAR72205 United States UALR/UAMS Joint Graduate Program in Bioinformatics Little RockAR72204 United States

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://***/NoduleX. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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Precision Rehabilitation for Patients Post-Stroke based on Electronic Health Records and Machine Learning

arXiv

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arXiv 2024年

作者： Gao, Fengyi Zhang, Xingyu Sivarajkumar, Sonish Denny, Parker E. Aldhahwani, Bayan M. Visweswaran, Shyam Shi, Ryan Hogan, William Bove, Allyn Wang, Yanshan Department of Health Information Management University of Pittsburgh PittsburghPA United States Department of Communication Science and Disorders University of Pittsburgh PittsburghPA United States Intelligent Systems Program School of Computing and Information University of Pittsburgh PittsburghPA United States Department of Physical Therapy University of Pittsburgh PittsburghPA United States Department of Medical Rehabilitation Sciences Umm Al-Qura University Makkah Saudi Arabia Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States Department of Computer Science University of Pittsburgh Medical Center PittsburghPA United States Data Science Institute Medical College of Wisconsin MilwaukeeWI United States Hillman Cancer Center University of Pittsburgh Medical Center PittsburghPA United States

Objective In this study, we utilized statistical analysis and machine learning methods to examine whether rehabilitation exercises can improve patients post-stroke functional abilities, as well as forecast the improvement in functional abilities. Our dataset is patients’ rehabilitation exercises and demographic information recorded in the unstructured electronic health records (EHRs) data and free-text rehabilitation procedure notes. Through this study, our ultimate goal is to pinpoint the specific rehabilitation exercises that can effectively aid post-stroke patients in improving their functional outcomes in basic mobility (BM) and applied cognitive (AC) domains. Data sources We collected data for 265 stroke patients from the University of Pittsburgh Medical Center, accessed through the Rehabilitation Datamart With Informatics iNfrastructure for Research (ReDWINE). Methods We employed a pre-existing natural language processing (NLP) algorithm to extract data on rehabilitation exercises and developed a rule-based NLP algorithm to extract Activity Measure for Post-Acute Care (AM-PAC) scores, covering basic mobility (BM) and applied cognitive (AC) domains, from procedure notes. AM-PAC scores were collected at the initial rehabilitation visit and followed up at one and two months—key recovery periods. Changes in AM-PAC scores were classified based on the minimal clinically important difference (MCID), and significance was assessed using Friedman and Wilcoxon tests. To identify impactful exercises, we used Chi-square tests, Fisher's exact tests, and logistic regression for odds ratios. Additionally, we developed five machine learning models—logistic regression (LR), Adaboost (ADB), support vector machine (SVM), gradient boosting (GB), and random forest (RF)—to predict outcomes in functional ability. Results Statistical analyses revealed significant associations between functional improvements and specific exercises. In the AC domain, the BALANCE exercise showed substant

关键词： Statistical tests

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Photonic Terahertz Phased Array

arXiv

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arXiv 2024年

作者： Niu, Li Feng, Xi Zhang, Xueqian Lu, Yongchang Wang, Qingwei Xu, Quan Chen, Xieyu Ma, Jiajun Qiu, Haidi Sha, Wei E.I. Zhang, Shuang Alù, Andrea Zhang, Weili Han, Jiaguang Center for Terahertz Waves College of Precision Instrument and Optoelectronics Engineering The Key Laboratory of Optoelectronics Information and Technology Tianjin University Tianjin300072 China Key Laboratory of Micro-nano Electronic Devices and Smart Systems of Zhejiang Province College of Information Science & Electronic Engineering Zhejiang University Hangzhou310027 China Department of Electrical & Electronic Engineering University of Hong Kong 999077 Hong Kong Photonics Initiative Advanced Science Research Center City University of New York New York10031 United States Physics Program Graduate Center City University of New York New York10016 United States School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74078 United States Guangxi Key Laboratory of Optoelectronic Information Processing School of Optoelectronic Engineering Guilin University of Electronic Technology Guilin541004 China

Phased arrays are crucial in various technologies, such as radar and wireless communications, due to their ability to precisely control and steer electromagnetic waves. This precise control improves signal processing and enhances imaging performance. However, extending phased arrays to the terahertz (THz) frequency range has proven challenging, especially for high-frequency operation, broadband performance, two-dimensional (2D) phase control with large antenna arrays, and strong phase modulation. Here, we introduce a photonic platform to realize a THz phased array that bypasses the above challenges. Our method employs 2D phase coding with 2-bit across a broad THz frequency range from 0.8 to 1.4 THz. The core of our design is a pixelated nonlinear Pancharatnam-Berry metasurface driven by a spatially modulated femtosecond laser, allowing precise phase control of THz signals. We showcase the effectiveness of our method through four proof-of-concept applications: single beamforming, dual beamforming, imaging and vortex beam generation. The realized photonic platform provides a promising pathway for developing broadband phased arrays in the THz regime. Copyright © 2024, The Authors. All rights reserved.

关键词： Terahertz waves

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Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model

arXiv

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arXiv 2024年

作者： Zhou, Peng Wang, Jianmin Li, Chunyan Wang, Zixu Liu, Yiping Sun, Siqi Lin, Jianxin Wei, Leyi Cai, Xibao Lai, Houtim Liu, Wei Wang, Longyue Liu, Yuansheng Zeng, Xiangxiang College of Information Science and Engineering Hunan University Changsha China The Interdisciplinary Graduate Program in Integrative Biotechnology Yonsei University Incheon21983 Korea Republic of School of Informatics Yunnan Normal University Kunming650500 China Department of Computer Science University of Tsukuba Tsukuba3058577 Japan Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China Shanghai AI Laboratory Shanghai200232 China Centre for Artificial Intelligence driven Drug Discovery Faculty of Applied Science Macao Polytechnic University China School of Informatics Xiamen University Xiamen China Tencent AI Lab China

While various models and computational tools have been proposed for structure and property analysis of molecules, generating molecules that conform to all desired structures and properties remains a challenge. Here, we introduce a multi-constraint molecular generation large language model, TSMMG, which, akin to a student, incorporates knowledge from various small models and tools, namely, the 'teachers'. To train TSMMG, we construct a large set of text-molecule pairs by extracting molecular knowledge from these 'teachers', enabling it to generate novel molecules that conform to the descriptions through various text prompts. We experimentally show that TSMMG remarkably performs in generating molecules meeting complex, natural language-described property requirements across two-, three-, and four-constraint tasks, with an average molecular validity of over 99% and success ratio of 82.58%, 68.03%, and 67.48%, respectively. The model also exhibits adaptability through zero-shot testing, creating molecules that satisfy combinations of properties that have not been encountered. It can comprehend text inputs with various language styles, extending beyond the confines of outlined prompts, as confirmed through empirical validation. Additionally, the knowledge distillation feature of TSMMG contributes to the continuous enhancement of small models, while the innovative approach to dataset construction effectively addresses the issues of data scarcity and quality, which positions TSMMG as a promising tool in the domains of drug discovery and materials science. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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Accelerating Real-Time Imaging for Radiotherapy: Leveraging Multi-GPU Training with PyTorch

Accelerating Real-Time Imaging for Radiotherapy: Leveraging ...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Ruth Obe Brandt Kaufmann Kaelen Baird Sam Kadel Yasmin Soltani Mostafa Cham Matthias K. Gobbert Carlos A. Barajas Zhuoran Jiang Vijay R. Sharma Lei Ren Stephen W. Peterson Jerimy C. PoIf Dept. of Computer Science and of Software Engineering U. of Houston-Clear Lake USA Dept. of Mathematics and Statistics U. of San Francisco USA Dept. of Computer Science and of Mathematics Skidmore College USA Dept. of Computer Science and of Psychology Mount Holyoke College USA Dept. of Biomedical Engineering U. of Houston USA Dept. of Information Systems U. of Maryland Baltimore County Dept. of Mathematics and Statistics U. of Maryland Baltimore County Medical Physics Graduate Program Duke University USA Dept. of Radiation Oncology U. of Maryland School of Medicine USA Department of Radiation Oncology U. of Maryland School of Medicine USA Dept. of Physics U. of Cape Town South Africa H3D Inc. USA

Proton beam therapy is an advanced form of cancer radiotherapy that uses high-energy proton beams to deliver precise and targeted radiation to tumors. This helps to mit-igate unnecessary radiation exposure in healthy tissues. Real-time imaging of prompt gamma rays with Compton cameras has been suggested to improve therapy efficacy. However, the camera's non-zero time resolution leads to incorrect interaction classifications and noisy images that are insufficient for accurately assessing proton delivery in patients. To address the challenges posed by the Compton camera's image quality, machine learning techniques are employed to classify and refine the generated data. These machine-learning techniques include recurrent and feedforward neural networks. A PyTorch model was designed to improve the data captured by the Compton camera. This decision was driven by PyTorch's flexibility, powerful capabilities in handling sequential data, and enhanced G PU usage. This accelerates the model's computations on large-scale radiotherapy data. Through hyperparameter tuning, the validation accuracy of our PyTorch model has been improved from an initial 7 % to over 60 %. Moreover, the PyTorch Distributed Data Parallelism strategy was used to train the RNN models on multiple G PU s, which significantly reduced the training time with a minor impact on model accuracy.

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A web-based, hospital-wide health care-associated bloodstream infection surveillance and classification system: Development and evaluation

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JMIR Medical Informatics 2015年第3期3卷 e31页

作者： Tseng, Yi-Ju Wu, Jung-Hsuan Lin, Hui-Chi Chen, Ming-Yuan Ping, Xiao-Ou Sun, Chun-Chuan Shang, Rung-Ji Sheng, Wang-Huei Chen, Yee-Chun Lai, Feipei Chang, Shan-Chwen Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Computational Health Informatics Program Boston Children's Hospital Boston MA United States Department of Electrical Engineering National Taiwan University Taipei Taiwan Center for Infection Control National Taiwan University Hospital Taipei Taiwan National Taiwan University Hospital Information Systems Office Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Internal Medicine National Taiwan University Hospital and College of Medicine Taipei Taiwan National Health Research Institutes National Institute of Infectious Diseases and Vaccinology Miaoli Taiwan

Background: Surveillance of health care-associated infections is an essential component of infection prevention programs, but conventional systems are labor intensive and performance dependent. Objective: To develop an automatic surveillance and classification system for health care-associated bloodstream infection (HABSI), and to evaluate its performance by comparing it with a conventional infection control personnel (ICP)-based surveillance system. Methods: We developed a Web-based system that was integrated into the medical information system of a 2200-bed teaching hospital in Taiwan. The system automatically detects and classifies HABSIs. Results: In this study, the number of computer-detected HABSIs correlated closely with the number of HABSIs detected by ICP by department (n=20;r=.999 P<.001) and by time (n=14;r=.941;P<.001). Compared with reference standards, this system performed excellently with regard to sensitivity (98.16%), specificity (99.96%), positive predictive value (95.81%), and negative predictive value (99.98%). The system enabled decreasing the delay in confirmation of HABSI cases, on average, by 29 days. Conclusions: This system provides reliable and objective HABSI data for quality indicators, improving the delay caused by a conventional surveillance system. © 2020 JMIR Publications Inc. All rights reserved.

关键词： Health care-associated infection Infection control information systems Surveillance Web-based services

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Corrigendum to “Using remote sensing to calculate floating photovoltaic technical potential of a dam’s surface” [Sustain. Energy Technol. Assess. 41 (2020) 100799]

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Sustainable Energy Technologies and Assessments 2021年 43卷

作者： Ali Murat Ates Osman Salih Yilmaz Fatih Gulgen Manisa Celal Bayar University Faculty of Education Computer and Instructional Technologies Department Turkey Manisa Celal Bayar University Demirci Vocational School Geographical Information Systems Program Turkey Yildiz Technical University Faculty of Civil Engineering Geomatic Engineering Department Turkey

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Modelling and reducing fuel flow pulsation of a fuel-metering system during pump mode switching in a turbofan engine

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IOP Conference Series: Earth and Environmental Science 2019年第5期240卷

作者： S Masuda F Shimizu M Fuchiwaki K Tanaka Control Systems Engineering Department Research & Engineering Division Aero Engines Space & Defense Business Area IHI Corporation 229 Tonogaya Mizuho-machi Nishitama-gun Tokyo 190-1297 Japan Department of Computer Science and Systems Engineering Doctoral Program Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 kawazu Iizuka-shi Fukuoka 820-8502 Japan Department of Mechanical Information Science and Technology School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 kawazu Iizuka-shi Fukuoka 820-8502 Japan

In this research, the transient characteristics of a new type fuel metering system for aircraft turbofan engines using two types of pumps, a gear pump and a centrifugal pump, were studied by modelling and simulation. This new fuel metering system is capable of hydraulically unloading the operating state of each pump by the pump mode switching mechanism within the permissible range of the pump rotation speed and the fuel flow rate and it is possible to reduce the fuel temperature rise due to the pressure loss in the circuit in the operation region of the entire fuel system. In addition to this, by using a small centrifugal pump that can realize high discharge flow rate, it is possible to reduce weight compared to the conventional fuel system. However, when the pump mode is switched, unexpected fluctuation in the fuel flow rate can occur when a large change in the inlet pressure of the system occurs, the pressure control unit cannot follow the change, and the transient phenomenon cannot be suppressed. At this time, the pressure waveform generated at the time of changing the pump mode includes a high frequency component such that the pressure control unit, which plays a role of keeping the metering valve differential pressure constant, cannot follow up. Therefore, in this paper, we focused on the frequency of the pressure change waveform when the pump mode was changed and studied the model of the fuel system inlet pressure in the gear pump mode. Based on that model, we propose a method to reduce the frequency characteristics of the pressure change occurring at the time of pump mode change.

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Waters network's potential to transform environmental engineering education

Waters network's potential to transform environmental engine...

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114th Annual ASEE Conference and Exposition, 2007

作者： Eschenbach, Elizabeth Montgomery, Jami Johnson, James Brus, Chris Carlson, Patricia Giammar, Dan Grauer, Bette Hotaling, Liesl Oguntimein, Gbekeloluwa Safferman, Steven Wentling, Tim Humboldt State University United States Department of Environmental Resources Engineering Humboldt State University United States WATERS Network - CLEANER Project Office Network Howard University United States Department of Environmental Engineering College of Engineering Architecture and Computer Sciences Howard University United States American Academy of Environmental Engineers United States University of Iowa United States Program University of Iowa United States Rose-Hulman Institute of Technology United States Department of Rhetoric Rose-Hulman Institute of Technology United States National Research Council U. S. Air Force United States NASA's Classroom of the Future Wheeling WV United States Washington University United States Department of Energy Environmental and Chemical Engineering Washington University St. Louis United States Environmental Studies Program Center for Materials Innovation United States McPherson High School United States McPherson High School McPherson KS United States Stevens Institute of Technology United States Stevens Institute of Technology United States Morgan State University United States Michigan State University United States Biosystems and Agricultural Engineering Department Michigan State University United States University of Dayton United States Education Planning Committee National Center for Supercomputing Applications United States Department of Information Science Graduate School of Library and Information Science National Center for Supercomputing Applications University or Illinois United States Knowledge and Learning Systems Group NCSA United States

The WATERS Network (WATer and Environmental Research systems Network) will be an integrated real-time distributed observing system which will enable academic and government scientists, engineers, educators, and practitioners to advance effective management of our nation's water resources by understanding human interactions with water and the natural and built environment. WATERS will provide easily accessible real time environmental data as well as analysis tools to engineers, scientists, educators, K- graduate students and policymakers so they can better understand how water quantity, quality and related components of the hydrologic cycle are impacted by natural and human influences. The WATERS strategic plan will be completed by July 2007 and requires constituent input. This paper describes the draft Conceptual Design and the Education Plan of WATERS Network. This paper will highlight the potential impact of WATERS on undergraduate and graduate environmental engineering education in order to elicit input from the environmental engineering education community on how WATERS Network could better meet the future needs of undergraduate and graduate students and educators. More information can be found at ***. © American Society for Engineering Education, 2007.

关键词： Water resources

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REVOLUTIONIZING RADIOTHERAPY COMMUNICATION SKILLS TRAINING: AN INNOVATIVE CHATBOT-BASED PROTOTYPE APPROACH (SCIMORT)

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Journal of Medical Imaging and Radiation Sciences 2023年第3期54卷 S14-S15页

作者： Aniza, Azmi Nor Afiqah, Mohd Sukri Nur Ikhwan, Mustafa Kamal Muhamad Rozilawati, Ahmad Azmie, Yahya Noorazrul Safwan, Ahmad Fadzil Muhammad Adilah, Suhaimi Nani Noraini, Ahmad Wahid Izzuan, Hussin Saiful Liyana, Shuib Diagnostic Imaging and Radiotherapy Program Faculty of Health Sciences Universiti Kebangsaan Malaysia 50300 Kuala Lumpur Malaysia Department of Mathematical Sciences Faculty of Science and Technology Universiti Kebangsaan Malaysia 43600 Selangor Darul Ehsan Malaysia Department of Information Systems Faculty of Computer Science and Information Technology University of Malaya 50603 Kuala Lumpur Malaysia

OBJECTIVEThis study aimed to develop and evaluate the effectiveness of a chatbot-based prototype model called SCIMORT, designed to improve the communication skills of undergraduate students in Diagnostic Imaging and Radiotherapy at Universiti Kebangsaan Malaysia. MATERIALS & METHODSThis pilot and survey study involved the development of the SCIMORT module, which included the creation of a 3D virtual patient with visual and auditory cues. The SCIMORT module was evaluated by surveying 28 participants from the PDR cohorts. The questionnaire was designed to assess user acceptance and engagement of SCIMORT, and the expert validation of the questionnaire was assessed using Content Validity Ratio and Cronbach Alpha Coefficient. RESULTSA total of 28 participants from the PDR cohorts were enrolled in these projects. In the first phase, the virtual patient developed for SCIMORT exhibited visual cues such as blinking and eye movement, as well as auditory cues, including verbal responses and intonation of voice. During the second phase, expert validation of the questionnaire yielded favourable RESULTS: , with a Content Validity Ratio exceeding 83% and a Cronbach Alpha Coefficient surpassing 70%. However, both the control group (users' acceptance = 3.919 ± 1.245; users' engagement = 3.526 ± 1.270) and the Trial group (users' acceptance = 3.953 ± 1.114; users' engagement = 3.568 ± 1.142) showed neutral outcomes. CONCLUSIONThe SCIMORT module proved to be an effective tool for pre-clinical learning of communication skills in radiotherapy. However, the control and the Trial groups exhibited neutral results regarding users' acceptance and engagement. Further improvements are needed to make the module more interactive and adaptable to student needs.

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