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Factors and Reasons Associated with Low COVID-19 Vaccine Uptake among Highly Hesitant Communities in the US (vol 50, pg 262, 2022)

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AMERICAN JOURNAL OF INFECTION CONTROL 2022年第5期50卷 591-591页

作者： Khairat, Saif Zou, Baiming Adler-Milstein, Julia [a]Cecil G. Sheps Center for Health Services Research University of North Carolina at Chapel Hill Chapel Hill NC [b]Carolina Health Informatics Program University of North Carolina at Chapel Hill Chapel Hill NC [c]School of Nursing University of North Carolina at Chapel Hill Chapel Hill NC [d]Department of Biostatistics Gilling's School of Global Health University of North Carolina at Chapel Hill Chapel Hill NC [e]Center for Clinical Informatics and Improvement Research University of California – San Francisco San Francisco CA

Background The inability to achieve high COVID-19 vaccination rates can continue to have serious harm to our communities. Vaccine hesitancy is a major barrier towards high vaccination rates. We evaluated the relationship between COVID-19 vaccine uptake and vaccine hesitancy, and then examined whether community factors were associated with COVID-19 vaccine uptake and hesitancy. Methods We constructed and evaluated a cross-sectional, county-level dataset that included the levels of vaccination uptake and vaccine hesitancy, and population characteristics based on those included in the CDC's Social Vulnerability Index. Results Across 3142 US counties, vaccine hesitancy was significantly and negatively correlated with vaccine uptake rates (r = -0.06, P value <.01). The 2 predictors associated with a low vaccination level within highly hesitant communities were: no high school education (OR:0.70, P value <.001), and concern on vaccine availability and distribution (CVAC) (OR:0.00, P value <.001). The most common reason driving vaccine hesitancy was lack of trust in COVID-19 vaccines (55%), followed by concerns around side effects of the vaccine (48%), and lack of trust in government (46%). Conclusions COVID-19 vaccine hesitancy is a public health threat. Our findings suggest that low education levels are a major contributor to vaccine hesitancy and ultimately vaccination levels. Since education levels are not easily modifiable, our results suggest that policymakers would be best served by closing knowledge gaps to overcome negative perceptions of the vaccine through tailored interventions.

关键词： Education Vaccination Health Services Research Public Health

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Optimizing Indoor Vertical Farming with DDPG Reinforcement Learning: Enhancing Control of Nutrient Levels, Climate Conditions, and Plant Health

Optimizing Indoor Vertical Farming with DDPG Reinforcement L...

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International Conference on Technology, informatics, Management, Engineering, and Environment (TIME-E)

作者： Albert Good Fried Panggabean Rahmat Romadhon Ramot Lubis Endra Joelianto Doctoral Program in Engineering Physics Institut Teknologi Bandung Bandung Indonesia Faculty of Informatics and Electrical Engineering Institut Teknologi Del Toba Indonesia Engineering Physics Study Program Institut Teknologi Bandung Bandung Indonesia Instrumentation Control and Automation Research Group Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9798350364750

ISBN: (纸本)9798350364767

In indoor vertical farming systems, managing dynamic and complex environments poses significant challenges. Traditional control strategies often fall short in adapting to rapid changes in critical parameters such as nutrient levels, climate conditions, and plant health indicators. To address these challenges, this paper presents a novel approach for optimizing indoor vertical farming using Deep Deterministic Policy Gradient (DDPG), a reinforcement learning algorithm well-suited for environments with continuous action spaces. Our study focused on the control and monitoring of key parameters, including nutrient levels (pH and EC), climate conditions (temperature, humidity, light, and CO2 levels), and plant health indicators (biomass accumulation and leaf size structure). By defining a comprehensive state and action space, we simulated an indoor farming environment and trained the DDPG model to maximize crop yield and health.

关键词： Smart agriculture Precision agriculture Reinforcement learning Humidity Aerospace electronics Nutrients Temperature control informatics Monitoring Farming

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Multi-task Learning Graph Neural Networks for Cancer Prognosis Prediction with Genomic Data

Multi-task Learning Graph Neural Networks for Cancer Prognos...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Tsung-Wei Lin Sofia Ormazabal Arriagada Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taiwan International Graduate Program AIoT NTU Research Center for Information Technology Innovation Academia Sinica Department of Electrical Engineering NTU Center for Advanced Computing and Imaging in Biomedicine NTU Smart Medicine and Health Informatics Program NTU

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Providing robust prognosis predictions for cancers with limited data samples remains a challenge for precision oncology. In this study, we propose a novel approach that combines multi-task learning (MTL) and graph neural networks (GNNs) to address this issue. By representing gene-gene interactions as a graph network, our approach leverages multi-task learning to effectively capture the relationships of genes relevant to the oncogenesis and progression of breast, lung, and colon cancer. We demonstrate that our approach improves the cancer prognosis prediction for cancers with fewer samples, such as colon adenocarcinoma, by leveraging the shared gene-gene interactions across different cancer types, obtaining increases in the area under the precision-recall curve (AUPRC) of 24%. Our work contributes to the field of smart healthcare by demonstrating the potential of MTL and GNNs for enhancing cancer prognosis prediction, even with limited data samples.

关键词： Lungs Genomics Medical services Colorectal cancer Breast Multitasking Oncology Graph neural networks Prognostics and health management Cancer

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An Implementation of Web Based GIS for Distribution of Freshwater Fish Ponds 9

An Implementation of Web Based GIS for Distribution of Fresh...

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9th International Conference on Cyber and IT Service Management, CITSM 2021

作者： Yulfiperius Herawan Hayadi, B. Utami, Risnita Tri Firman Zulkhasyni Pardiansyah, Dedi Universitas Prof. Dr. Hazairin SH Aquaculture Study Program Faculty of Agriculture Bengkulu Indonesia Universitas Prof. Dr. Hazairin SH Informatics Study Program Faculty of Engineering Bengkulu Indonesia

ISBN: (纸本)9781665435482

Geographical Information System is a term in the field of mapping which has a scope of how a system can unify geographic objects and their information. Freshwater fishery is the business of the majority of residents of South Bengkulu Regency covering tilapia and catfish. The solution is to create a web-based Freshwater Fish Pond GIS application using the PHP programming language. Making a database using MySQL, and for geolocation storage using Google Fusion Tables. The web that will be created will later display the GIS Web-based Freshwater Fish Pond Distribution Mapping in South Bengkulu Regency. Each pond will be accompanied by information such as the name of the owner, type of fish, type of pond, and location of the pond. It is known that the number of ponds in 4 villages in 2 districts in South Bengkulu Regency has reached 100 ponds consisting of 80 tilapia fish ponds, 20 catfish ponds. © 2021 IEEE.

关键词： Geographic information systems

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Hand-Woven Fabric Motif Recognition Methods: A Systematic Literature Review 3

Hand-Woven Fabric Motif Recognition Methods: A Systematic Li...

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3rd International Conference on informatics, Multimedia, Cyber, and Information System, ICIMCIS 2021

作者： Noprisson, Handrie Ermatita, Ermatita Abdiansah, Abdiansah Ayumi, Vina Purba, Mariana Utami, Marissa Universitas Sriwijaya Doctoral Program in Engineering Palembang Indonesia Universitas Sriwijaya Faculty of Computer Science Palembang Indonesia Program of Informatics Management Politeknik Anika Palembang Indonesia Muhammadiyah University of Bengkulu Faculty of Engineering Bengkulu Indonesia

ISBN: (纸本)9781665427333

Knowledge of hand-woven motif recognition is only owned by the older generation, which has not been passed down to the younger generation. Moreover, computer technology can be used to support the recognition of traditional woven fabric motifs. The aim of this research is to conduct a literature review on hand-woven fabric motif recognition, with an emphasis on performance accuracy, techniques, and datasets utilized. This research included three distinct stages of systematic literature review (SLR): planning, execution, and reporting of findings. We found several datasets between 924,845 data and the number of classes between 3-25 class. Based on research result, we obtained several methods for hand-woven fabric motif recognition focused on performance examination. We recommended image pre-processing method, including Adaptive Filtering Denoising, Adaptive Wiener Filtering, Histogram Equalization, Gradient Pyramid (GP) Decomposition. Moreover, we suggested five feature extraction methods, including Radon Transform, Wavelet Transform, Locally Rotation Invariance Measure (LBP-ROR), Transform Invariant Low-Rank Textures (TILT) and Histogram of Oriented Gradients (HOG). For learning method, we recommend Fuzzy C-Mean (FCM), Convolutional neural network (CNN), Deep Neural Network (DNN), MobileNets, Inception-v3 and ResNet-50. © 2021 IEEE.

关键词： Weaving

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Backcasting Perspectives on Services of Future Quantum Internet

Backcasting Perspectives on Services of Future Quantum Inter...

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Quantum Software (QSW), IEEE International Conference on

作者： Shota Nagayama Toshihiko Sasaki Akihito Soeda Mercari R4D Mercari Inc Tokyo Japan Graduate School of Media and Governance Keio University Fujisawa Japan Department of Applied Physics Graduate School of Engineering The University of Tokyo Tokyo Japan Principles of Informatics Research Division National Institute of Informatics Tokyo Japan Informatics Program SOKENDAI (The Graduate University for Advanced Studies) Tokyo Japan Department of Physics Graduate School of Science The University of Tokyo Tokyo Japan

ISBN: (数字)9798350368475

ISBN: (纸本)9798350368482

A quantum internet is an emerging quantum information technology, possibly becoming a pinnacle of wide-area distributed system allowed under the laws of physics. The current expansion of quantum technologies would lead to innumerable types of quantum systems, composed of various kinds of quantum devices connected by a dedicated quantum network. However, the (inter)connectivity offered by a quantum internet should ideally be agnostic of the technologies used by each component system. Furthermore, concurrent progresses of the quantum technologies complicate the task of defining what a quantum internet is. In this paper, we adopt a backcasting approach to elucidate what a quantum internet should be. We begin by discussing what we believe to be the requirements of an ultimate distributed quantum system, from which we explain what role a quantum internet would play in achieving this ultimate system. We then lead to more practical requirements on a quantum internet, considering the known and expected constraints in designing a quantum internet. We also organize and categorize the relationship among known quantum network technologies and services relevant to a quantum internet. We hope this document to lay a common ground for all the key stakeholders of a quantum internet, including researchers, industrial enterprises, and policy makers and regulators.

关键词： Quantum system Protocols Quantum mechanics Transportation Logic gates Internet Quantum networks

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ATP_mCNN: Predicting ATP binding sites through pretrained language models and multi-window neural networks

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Computers in Biology and Medicine 2025年 185卷 109541-109541页

作者： Le, Van-The Malik, Muhammad-Shahid Lin, Yi-Jing Liu, Yu-Chen Chang, Yan-Yun Ou, Yu-Yen Department of Computer Science and Engineering Yuan Ze University Chung-Li32003 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Chung-Li32003 Taiwan Department of Computer Sciences Karakoram International University Gilgit-Baltistan 15100 Pakistan

Adenosine triphosphate plays a vital role in providing energy and enabling key cellular processes through interactions with binding proteins. The increasing amount of protein sequence data necessitates computational methods for identifying binding sites. However, experimental identification of adenosine triphosphate-binding residues remains challenging. To address the challenge, we developed a multi-window convolutional neural network architecture taking pre-trained protein language model embeddings as input features. In particular, multiple parallel convolutional layers scan for motifs localized to different window sizes. Max pooling extracts salient features concatenated across windows into a final multi-scale representation for residue-level classification. On benchmark datasets, our model achieves an area under the ROC curve of 0.95, significantly improving on prior sequence-based models and outperforming convolutional neural network baselines. This demonstrates the utility of pre-trained language models and multi-window convolutional neural networks for advanced sequence-based prediction of adenosine triphosphate-binding residues. Our approach provides a promising new direction for elucidating binding mechanisms and interactions from primary structure. © 2024 Elsevier Ltd

关键词： Convolutional neural networks

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Spectral Topological Data Analysis of Brain Signals

arXiv

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arXiv 2023年

作者： El-Yaagoubi, Anass B. Jiao, Shuhao Chung, Moo K. Ombao, Hernando Statistics Program King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Biostatistics & Medical Informatics University of Wisconsin–Madison MadisonWI United States

Topological data analysis (TDA) has become a powerful approach over the last twenty years, mainly due to its ability to capture the shape and the geometry inherent in the data. Persistence homology, which is a particular tool in TDA, has been demonstrated to be successful in analyzing functional brain connectivity. One limitation of standard approaches is that they use arbitrarily chosen threshold values for analyzing connectivity matrices. To overcome this weakness, TDA provides a filtration of the weighted brain network across a range of threshold values. However, current analyses of the topological structure of functional brain connectivity primarily rely on overly simplistic connectivity measures, such as the Pearson correlation. These measures do not provide information about the specific oscillators that drive dependence within the brain network. Here, we develop a frequency-specific approach that utilizes coherence, a measure of dependence in the spectral domain, to evaluate the functional connectivity of the brain. Our approach, the spectral TDA (STDA), has the ability to capture more nuanced and detailed information about the underlying brain networks. The proposed STDA method leads to a novel topological summary, the spectral landscape, which is a 2D-generalization of the persistence landscape. Using the novel spectral landscape, we analyze the EEG brain connectivity of patients with attention deficit hyperactivity disorder (ADHD) and shed light on the frequency-specific differences in the topology of brain connectivity between the controls and ADHD patients. Copyright © 2023, The Authors. All rights reserved.

关键词： Time series analysis

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Design Enterprise Architecture Using the TOGAFADM Framework to Support Academic Information Systems at Nusa Nipa University

Design Enterprise Architecture Using the TOGAFADM Framework ...

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Smart Technology (ICE-SMARTec), IEEE International Conference on

作者： Yohanes Brekmans M. Darkel Andi W. R. Emanuel Paulus Mudjihartono Faculty of Engineering Informatics Engineering Study Program Universitas Nusa Nipa Indonesia Departemen Informatika Fakultas Teknologi Industri Universitas Atma Jaya Yogyakarta Yogyakarta Indonesia

Information technology support is desperately needed for higher education. Universities must use information technology to effectively and efficiently manage potential resources to compete with other institutions. Utilization of IT in Higher Education Governance, particularly the academic information system at Nusa Nipa University, lacks a structured architecture. The current IT infrastructure is insufficient to respond promptly and precisely to application and business change solutions. This study aims to simplify the process of developing information system architecture by developing an architectural model and an implementation roadmap. This study's design uses the TOGAF ADM model, The organization's initial condition analysis stage is the preliminary requirement management phase; architectural vision; and business architecture. The enterprise architecture design begins with the second stage's information system architecture and technology architecture phases. The subsequent phases of opportunities and solutions, migration planning, and change management, recommendations for implementing enterprise architecture are presented in the third section. The consequences of this study are IT outlines for each engineering stage given the TOGAF ADM structure for business processes in new understudy affirmations frameworks, scholastic cycles, graduated classes, and libraries to help data frameworks at Nusa Nipa College. This research can create a blueprint for IT development suggestions for expected business techniques at Nusa Nipa College.

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Development of Automated Detection Methods for Calculating Blood Vessel Width and Stenosis Rate in Cardiac Catheterization Imaging 24

Development of Automated Detection Methods for Calculating B...

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8th International Conference on Medical and Health informatics, ICMHI 2024

作者： Huang, Hsien-Cheng Chang, Hsiao-Huang Ke, Xiang-Wei Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Cardiovascular Surgery Department Taipei Veterans General Hospital Taipei City Taiwan Taipei Medical University Taipei City Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Coronary artery disease is the most prevalent type of heart disease and has a considerable mortality rate. In diagnosing and assessing coronary artery disease, physicians must integrate a variety of clinical information and test results to evaluate the degree of coronary artery obstruction and continually learn and improve their skills in clinical practice to ensure the most accurate assessment of patients. Currently, the use of information technology to assist medical staff allows more effective and accurate analysis of symptoms. This approach not only serves as an auxiliary standard for physician diagnosis, thereby reducing the possibility of misdiagnosis, but also significantly improves patient treatment outcomes and medical quality while reducing the time burden on physicians for diagnosing patients. This study utilized image processing techniques and thinning algorithms to automate the detection of coronary artery width and, following the recommended methods of physicians in related fields, used specific vessel widths to calculate the degree of coronary artery narrowing. This provides a more accurate and efficient assessment method for evaluating the degree of vascular constriction in patients with coronary artery disease. © 2024 Copyright ACM.

关键词： Patient treatment

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