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Celebrity disclosure prompts public discourse regarding appearance changes related to breast cancer care: a content analysis of Internet comments

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Journal of Communication in Healthcare 2020年第1期13卷 64-71页

作者： Gordon, Janna R. Dial, Lauren A. Mills, Sarah D. Drizin, Julia H. Barnes, Laura E. Malcarne, Vanessa L. Klonoff, Elizabeth Vaughn, Allison Wells, Kristen J. San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology San Diego CA United States Psychology Department Bowling Green State University Bowling Green OH United States Lineberger Comprehensive Cancer Center University of North Carolina Carrboro NC United States College of Public Health and Human Services Oregon State University Corvallis OR United States Systems and Information Engineering University of Virginia Charlottesville VA United States Department of Psychology San Diego State University San Diego CA United States Moores Cancer Center University of California San Diego La Jolla CA United States College of Graduate Studies University of Central Florida Orlando FL United States

Background: In an op-ed posted on the New York Times (NYT) website, Angelina Jolie disclosed that she carries the BRCA1 genetic mutation and received a preventive mastectomy. Studies have since demonstrated a positive impact of Ms. Jolie's disclosure on breast cancer-related health behaviors. This qualitative study used conventional content analysis of comments to the op-ed to explore intermediate outcomes of the disclosure;specifically, how the disclosure influenced public discourse regarding appearance changes related to breast cancer surgery. Method: After downloading all comments from the NYT website and using an iterative process to develop a standardized codebook, two researchers applied the codes using NVivo 10. Result: Five subthemes were identified in sixty-one comments discussing physical appearance or body image in the context of breast cancer treatment or preventive care: appearance versus health, perception of breast appearance following surgery, partner acceptance of appearance following surgery, beliefs about others’ reactions regarding appearance, and beliefs about reconstructive surgery. While some commenters shared body image concerns following surgery, many were relieved to have undergone the procedure, valuing health over appearance. Most women stated that their romantic partners praised their appearance following surgery, although some worried about how they were perceived in public. Conclusion: This study clarifies how celebrity disclosures about cancer may influence intermediate outcomes (e.g. interpersonal communication and beliefs about appearance-related surgical outcomes) hypothesized to contribute to changes in cancer prevention and treatment behaviors. More research is needed to further elucidate the relationships between celebrity disclosure, intermediate outcomes, and behavioral outcomes. © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group.

关键词： appearance concerns body image Breast neoplasms celebrity disclosure mass media mastectomy online discussion online forum

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Analysis of the Passive Fragments Influence in Broadband Signals with Manipulated information Entropy on the Data Transmission Reliability

Analysis of the Passive Fragments Influence in Broadband Sig...

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International Scientific-Practical Conference Problems of Infocommunications Science and Technology

作者： Stepan Melnychuk Iryna Manuliak Computer systems and networks department of Ivano-Frankivsk National Technical University of Oil and Gas Ivano-Frankivsk Ukraine Department of Information Technology and Program Engineering of King Danylo University Ivano-Frankivsk Ukraine

ISBN: (数字)9781728141848

ISBN: (纸本)9781728141855

Current methods of digitally processing communication signals, which are distorted in the computer systems communication channels, are often based on the use of amplitude, phase and spectral characteristics. Correlation methods are also used to ensure immunity. A common disadvantage of these methods is the need for significant computational cost. One of the partial solutions to the described problem may be to use the signals probabilistic characteristics, in particular information entropy. This approach is based on the representation of digital message bits to the corresponding fragments of broadband signals with manipulated information entropy. In fact, signals are formed on the basis of random processes with normal distribution of possible states. The use of passive fragments in such signals can reduce the energy cost of data transmission. When passing through a communication channel, passive fragments are filled with their own channel noise. As a result, the channel's own noise extends the number of possible signal amplitude states and can be used to improve the resolution. According to the results of studies, the dependence of the probability of distortion of the information message binary symbol on the normalized signal/noise ratio is obtained. It is shown that the use of passive fragments improves noise immunity by 3-4 dB within the normalized signal/noise ratio at the input from -5 dB to 14 dB. However, at 14 dB or more, noise immunity worsens. Thus, the main scope of the proposed method can be problematic channels with low signal/noise ratio.

关键词： Distortion information entropy Estimation Lead Interference Probability Broadband communication

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Untitled

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SCIENCE 2022年第6615期378卷 22-22页

作者： Sills, Jennifer Division of Life Sciences and Medicine University of Science and Technology of China Hefei Anhui 230027 China. Department of Surgery Harvard University Brigham and Women’s Hospital Boston MA 02115 USA. Faculty of Applied Science Macao Polytechnic University Macao SR China. School of Liberal Education Chengdu Jincheng College Chengdu Sichuan 611731 China. Translational Health Science and Technology Institute Faridabad Haryana 121001 India. Department of Chemical Engineering University of Washington Seattle WA 98105 USA. Johns Hopkins University Baltimore MD 21218 USA. The Affiliated Hospital Guizhou Medical University Guiyang Guizhou 550004 China. Department of Infectious Disease Epidemiology Imperial College London London W2 1PG UK. Manchester Fungal Infection Group University of Manchester Manchester UK. Philadelphia PA USA. Harrisburg PA 17102 USA. Department of Chemistry University of Pennsylvania Philadelphia PA 19104 USA. University of Kansas Lawrence KS 66045 USA. Mott MacDonald Noida UP 201010 India. Trudeau Institute Saranac Lake NY 12983 USA. US Department of Agriculture Parlier CA 93648 USA. Vagelos Molecular Life Sciences Program University of Pennsylvania Philadelphia PA 19104 USA. School of Information Systems and Technology Management University of New South Wales Sydney NSW 2052 Australia.

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Model of developing key performance indicator to increase the quality of education during the Covid-19 pandemic

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Journal of Physics: Conference Series 2021年第1期1832卷

作者： E Selviyanti W Sardjono M Mukhlis M Tohir M Maswar A Fariz Health Department Politeknik Negeri Jember Jember Jawa Timur Indonesia 68124 Information Systems Management Department BINUS Graduate Program – Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 Faculty of Teacher Training and Education Islamic State Institute of Jember Jawa Timur INDONESIA Universitas Ibrahimy Situbondo Indonesia Computer Network Engineering SMK 3 Perguruan 'Cikini' Jakarta Indonesia 12640

Indonesia is one of the countries whose people are most affected by the 2019 corona virus (COVID-19). When the corona virus spreads faster, the Indonesian government imposed large-scale social restrictions in big cities to villages in Indonesia. With the changing situation in carrying out work activities during this pandemic, it will certainly affect the quality of organization in companies and the quality of education in schools and colleges. In order to identify all processes in teaching and learning activities that are running well or not, schools need to monitor and measure employee performance. Key performance indicators (KPIs) are generally used as benchmarks to determine how well a company's performance can be represented by employee performance. In this study, the key performance indicator preparation process was carried out through the stages of designing a questionnaire, distributing questionnaires to employees in educational institutions, conducting factor analysis and then developing a KPI model. The output is in the form of a KPI model that can be used as a reference in the formation of KPIs in educational institutions, namely Y = 5,360 + 1,380 X1-1, 068 X3.

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A Study on Time Efficiency of CSMA/CA in Inter-Vehicle Communication systems

A Study on Time Efficiency of CSMA/CA in Inter-Vehicle Commu...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Masashi Kato Masahiro Fujii Graduate School of Engineering Utsunomiya University 7-1-2 Yoto Utsunomiya Tochigi Tochigi Japan Graduate Program in Information Electrical and Electronics Systems Engineering Utsunomiya University 7-1-2 Yoto Utsunomiya Tochigi Tochigi Japan

ISBN: (数字)9781728135755

ISBN: (纸本)9781728135762

CSMA/CA has been widely accepted as a Media Access Control scheme for Inter-Vehicle Communication systems. In CSMA/CA, a mobile station waits a frame transmission during a backoff interval based on random value. Then, the mobile station transmits the frame if the others do not transmit any frames. In this paper, we evaluate system performances of CSMA/CA from the viewpoint of a time efficiency and average frame delay time varying the range of random value by network simulations.

关键词： Access protocols Access control Telecommunication traffic Multiaccess communication Media Access Protocol Wireless communication

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Assessing progress towards sustainable development over space and time (vol 577, pg 74, 2020)

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NATURE 2021年第7856期592卷 E28-E28页

作者： Xu, Zhenci Chau, Sophia N. Chen, Xiuzhi Zhang, Jian Li, Yingjie Dietz, Thomas Wang, Jinyan Winkler, Julie A. Fan, Fan Huang, Baorong Li, Shuxin Wu, Shaohua Herzberger, Anna Tang, Ying Hong, Dequ Li, Yunkai Liu, Jianguo Center for Systems Integration and Sustainability Department of Fisheries and Wildlife Michigan State University East Lansing MI USA Environmental Science and Policy Program Department of Sociology and Animal Studies Program Michigan State University East Lansing MI USA Department of Geography Environment and Spatial Sciences Michigan State University East Lansing MI USA College of Water Resources and Civil Engineering China Agricultural University Beijing China School of Life Sciences State Key Laboratory of Grassland Agro-ecosystems Lanzhou University Lanzhou China School of Economics Renmin University of China Beijing China Institutes of Science and Development Chinese Academy of Sciences Beijing China Institute of Land and Urban-Rural Development Zhejiang University of Finance and Economics Hangzhou China Department of Humanities and Information Zhejiang College of Construction Hangzhou China

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Left ventricular hypertrophy detection using electrocardiographic signal

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Scientific reports 2023年第1期13卷 2556页

作者： Cheng-Wei Liu Fu-Hsing Wu Yu-Lun Hu Ren-Hao Pan Chuen-Horng Lin Yung-Fu Chen Guo-Shiang Tseng Yung-Kuan Chan Ching-Lin Wang Division of Cardiology Department of Internal Medicine Tri-Service General Hospital Songshan Branch National Defense Medical Center Taipei Taiwan. Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung Taiwan. Department of Management Information Systems National Chung-Hsing University Taichung Taiwan. La Vida Tec. Co. Ltd. Taichung Taiwan. Preventive Medicine Center National Yang Ming Chiao Tung University Taipei Taiwan. Department of Information Management Tunghai University Taichung Taiwan. Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan. Department of Dental Technology and Materials Science Central Taiwan University of Science and Technology Taichung Taiwan. Division of Cardiology Department of Internal Medicine Taoyuan Armed Force General Hospital Hsinchu Branch Hsinchu Taiwan. Department of Management Information Systems National Chung-Hsing University Taichung Taiwan. ykchan@nchu.edu.tw. Department of Information Management National Chin-Yi University of Technology Taichung Taiwan. clwang@ncut.edu.tw.

Left ventricular hypertrophy (LVH) indicates subclinical organ damage, associating with the incidence of cardiovascular diseases. From the medical perspective, electrocardiogram (ECG) is a low-cost, non-invasive, and easily reproducible tool that is often used as a preliminary diagnosis for the detection of heart disease. Nowadays, there are many criteria for assessing LVH by ECG. These criteria usually include that voltage combination of RS peaks in multi-lead ECG must be greater than one or more thresholds for diagnosis. We developed a system for detecting LVH using ECG signals by two steps: firstly, the R-peak and S-valley amplitudes of the 12-lead ECG were extracted to automatically obtain a total of 24 features and ECG beats of each case (LVH or non-LVH) were segmented; secondly, a back propagation neural network (BPN) was trained using a dataset with these features. Echocardiography (ECHO) was used as the gold standard for diagnosing LVH. The number of LVH cases (of a Taiwanese population) identified was 173. As each ECG sequence generally included 8 to 13 cycles (heartbeats) due to differences in heart rate, etc., we identified 1466 ECG cycles of LVH patients after beat segmentation. Results showed that our BPN model for detecting LVH reached the testing accuracy, precision, sensitivity, and specificity of 0.961, 0.958, 0.966 and 0.956, respectively. Detection performances of our BPN model, on the whole, outperform 7 methods using ECG criteria and many ECG-based artificial intelligence (AI) models reported previously for detecting LVH.

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Diffusive topological transport in spatiotemporal thermal lattices

arXiv

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arXiv 2021年

作者： Xu, Guoqiang Yang, Yihao Zhou, Xue Chen, Hongsheng Alù, Andrea Qiu, Cheng-Wei Department of Electrical and Computer Engineering National University of Singapore Kent Ridge117583 Singapore Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou China School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing400067 China ZJU-Hangzhou Global Science and Technology Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Zhejiang University Hangzhou310027 China International Joint Innovation Center ZJU-UIUC Institute The Electromagnetics Academy at Zhejiang University Zhejiang University Haining314400 China Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center of the City University of New York New YorkNY10016 United States

Topological insulating phases are usually found in periodic lattices stemming from collective resonant effects, and it may thus be expected that similar features may be prohibited in thermal diffusion, given its purely dissipative and largely incoherent nature. We report the diffusion-based topological states supported by spatiotemporally-modulated advections stacked over a fluidic surface, thereby imitating a periodic propagating potential in effective thermal lattices. We observe edge and bulk states within purely nontrivial and trivial lattices, respectively. At interfaces between these two types of lattices, the diffusive system exhibits interface states, manifesting inhomogeneous thermal properties on the fluidic surface. Our findings establish a framework for topological diffusion and thermal edge/bulk states, and it may empower a distinct mechanism for flexible manipulation of robust heat and mass transfer. © 2021, CC BY.

关键词： Interface states

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Parameter Optimization and Learning in a Spiking Neural Network for UAV Obstacle Avoidance targeting Neuromorphic Processors

arXiv

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arXiv 2019年

作者： Salt, Llewyn Howard, David Indiveri, Giacomo Sandamirskaya, Yulia School of Information Technology and Electrical Engineering University of Queensland QLD Australia Robotics and Autonomous Systems Group in the Cyberphysical System Program CSIRO QLD Australia Institute of Neuroinformatics University of Zurich ETH Zurich Zurich Switzerland

he Lobula Giant Movement Detector (LGMD) is an identified neuron of the locust that detects looming objects and triggers the insect’s escape responses. Understanding the neural principles and network structure that lead to these fast and robust responses can facilitate the design of efficient obstacle avoidance strategies for robotic applications. Here we present a neuromorphic spiking neural network model of the LGMD driven by the output of a neuromorphic Dynamic Vision Sensor (DVS), which incorporates spiking frequency adaptation and synaptic plasticity mechanisms, and which can be mapped onto existing neuromorphic processor chips. However, as the model has a wide range of parameters, and the mixed signal analogue-digital circuits used to implement the model are affected by variability and noise, it is necessary to optimise the parameters to produce robust and reliable responses. Here we propose to use Differential Evolution (DE) and Bayesian Optimisation (BO) techniques to optimise the parameter space and investigate the use of Self-Adaptive Differential Evolution (SADE) to ameliorate the difficulties of finding appropriate input parameters for the DE technique. We quantify the performance of the methods proposed with a comprehensive comparison of different optimisers applied to the model, and demonstrate the validity of the approach proposed using recordings made from a DVS sensor mounted on a UAV. Copyright © 2019, The Authors. All rights reserved.

关键词： Neural networks

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Multi-label classification for multi-temporal, multi-spatial coral reef condition monitoring using vision foundation model with adapter learning

arXiv

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arXiv 2025年

作者： Shao, Xinlei Chen, Hongruixuan Zhao, Fan Magson, Kirsty Chen, Jundong Li, Peiran Wang, Jiaqi Sasaki, Jun Department of Socio-Cultural Environmental Studies Graduate School of Frontier Sciences The University of Tokyo Chiba Kashiwa277-8563 Japan Department of Complexity Science and Engineering Graduate School of Frontier Sciences The University of Tokyo Chiba Kashiwa277-8563 Japan Department of Environment Systems Graduate School of Frontier Sciences The University of Tokyo Chiba Kashiwa277-8563 Japan New Heaven Reef Conservation Program Koh Tao Surat Thani84360 Thailand Data Science and AI Innovation Research Promotion Center Shiga University Shiga Hikone522-8522 Japan Interfaculty Initiative in Information Studies The University of Tokyo Tokyo113-0033 Japan

Coral reef ecosystems provide essential ecosystem services, but face significant threats from climate change and human activities. Although advances in deep learning have enabled automatic classification of coral reef conditions, conventional deep models struggle to achieve high performance when processing complex underwater ecological images. Vision foundation models, known for their high accuracy and cross-domain generalizability, offer promising solutions. However, fine-tuning these models requires substantial computational resources and results in high carbon emissions. To address these challenges, adapter learning methods such as Low-Rank Adaptation (LoRA) have emerged as a solution. This study introduces an approach integrating the DINOv2 vision foundation model with the LoRA fine-tuning method. The approach leverages multi-temporal field images collected through underwater surveys at 15 dive sites at Koh Tao, Thailand, with all images labeled according to universal standards used in citizen science-based conservation programs. The experimental results demonstrate that the DINOv2-LoRA model achieved superior accuracy, with a match ratio of 64.77%, compared to 60.34% achieved by the best conventional model. Furthermore, incorporating LoRA reduced the trainable parameters from 1,100M to 5.91M. Transfer learning experiments conducted under different temporal and spatial settings highlight the exceptional generalizability of DINOv2-LoRA across different seasons and sites. This study is the first to explore the efficient adaptation of foundation models for multi-label classification of coral reef conditions under multi-temporal and multi-spatial settings. The proposed method advances the classification of coral reef conditions and provides a tool for monitoring, conserving, and managing coral reef ecosystems. © 2025, CC BY-NC-SA.

关键词： Biotic

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