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Molecular epidemiology and phylogenetic analysis of influenza viruses A (H3N2) and B/ Victoria during the COVID-19 pandemic in Guangdong, China

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Infectious Diseases of Poverty 2024年第4期13卷 99-100页

作者： Zhiqi Zeng Yong Liu Wenxiang Jin Jingyi Liang Jinbin Chen Ruihan Chen Qianying Li Wenda Guan Lixi Liang Qiubao Wu Yuanfang Lai Xiaoyan Deng Zhengshi Lin Chitin Hon Zifeng Yang State Key Laboratory of Respiratory Disease National Clinical Research Center for Respiratory Disease Guangzhou Institute of Respiratory Health the First Affiliated Hospital of Guangzhou Medical University Guangzhou Guangdong 510180 P.R. China Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases KingMed School of Laboratory Medicine Guangzhou Medical University Guangzhou People’s Republic of China Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovative Engineering Macau University of Science and Technology Macau SAR China Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Taipa Macau China Guangzhou Laboratory Guangzhou China Kingmed Virology Diagnostic and Translational Center Guangzhou Kingmed Center for Clinical Laboratory Co. Ltd. Guangzhou China

Background Non-pharmaceutical measures and travel restrictions have halted the spread of coronavirus disease 2019(COVID-19)and ***,with COVID-19 restrictions lifted,an unanticipated outbreak of the influ-enza B/Nictoria virus in late 2021 and another influenza H3N2 outbreak in mid-2022 occurred in Guangdong,southern *** mechanism underlying this phenomenon remains *** better prepare for potential influenza out-breaks during COVID-19 pandemic,we studied the molecular epidemiology and phylogenetics of influenza A(H3N2)and BNictoria that circulated during the COVID-19 pandemic in this *** From January 1,2018 to December 31,2022,we collected throat swabs from 173,401 patients in Guang-dong who had acute respiratory tract *** viruses in the samples were tested using reverse tran-scription-polymerase chain reaction,followed by subtype identification and sequencing of hemagglutinin(HA)and neuraminidase(NA)*** and genetic diversity analyses were performed on both genes from 403 samples.A rigorous molecular clock was aligned with the phylogenetic tree to measure the rate of viral evolution and the root-to-tip distance within strains in different years was assessed using regression curve models to determine the *** During the early period of COVID-19 control,various influenza viruses were nearly undetectable in respiratory *** control measures were relaxed in January 2020,the influenza infection rate peaked at 4.94%(39/789)in December 2021,with the influenza B/Victoria accounting for 87.18%(34/39)of the total influenza *** months later,the influenza infection rate again increased and peaked at 11.34%(255/2248)in June 2022;influenza A/H3N2 accounted for 94.51%(241/255)of the total influenza cases in autumn *** diverse geographic distribution of HA genes of B/Nictoria and A/H3N2 had drastically reduced,and most strains originated from *** rate of B/Victoria HA e

关键词： Insidious transmission Influenza COVID-19 control Genetic diversity Evolution

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Photonics Applications and Web engineering - WILGA 2022

Photonics Applications and Web Engineering - WILGA 2022

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Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2022

作者： Romaniuk, Ryszard S. Smolarz, Andrzej Wójcik, Waldemar Warsaw University of Technology Institute of Electronic Systems Poland Lublin University of Technology Faculty of Electrical Engineering and Computer Science Poland

ISBN: (纸本)9781510660595

Wilga 2022 Summer Symposium on Photonics Applications and Web engineering was the 50th edition of the research and technical meetings series. Traditionally, the whole week lasting annual series of technical conferences and topical sessions was split to one winter meeting in January and two summer events in May/June, and in September 2022. The hybrid meetings were held in Wilga resort near Warsaw, owned by the Warsaw University of technology and in Lublin. Nearly 100 participants took part in all of the Wilga 2022 events, most of them being young researchers active in all aspects of photonics, electronics and ICT science and technology. Wilga Symposium embraces hardware and software technologies associated with photonics like optics, optical engineering, optoelectronics, electronics and electrical engineering, mechatronics, chemical and material engineering, applied physics, industrial solutions and applications. Around 100 papers were presented during Wilga 2022, oral and poster. Out of this number, some papers chosen by authors are published in this volume of Proc. SPIE mostly related to photonics. The major change was that during Wilga 2020, 2021 and 2022 Symposia, during summer editions, no traditional large young researcher sessions at large were possible to be organized. © 2022 SPIE. All rights reserved.

关键词： Photonics

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MyoRCB-CAM-Seg: A New Supervised Segmentation Mechanism for Diagnosis of Myositis medical ultrasound images Integrating new Modified Convolutional Residual Blocks and Convolutional Attention Mechanism

MyoRCB-CAM-Seg: A New Supervised Segmentation Mechanism for ...

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Machine Intelligence and Smart Innovation (ICMISI), International Conference on

作者： Alaa Hussien Sherin Youssef Noha Ghatwary Magdy A. Ahmed Computer Engineering Department Pharos University Alexandria Egypt Computer Engineering Department Arab Academy for Science and Technology Alexandria Egypt Department of Computer Engineering Faculty of Engineering Alexandria University Egypt

ISBN: (数字)9798350365740

ISBN: (纸本)9798350365757

Myositis is an extremely uncommon condition that causes muscle weakening due to an immune system attack on muscle cells. An accurate and timely diagnosis is essential for successful treatment. Differentiating among various kinds of myositis remains a big problem, leading to incorrect diagnoses and delayed treatment. Segmentation can improve the visualisation of medical ultrasound images by highlighting the region of interest and reducing the image's complexity by reducing the quantity of data that must be processed. This research presents a a unique supervised segmentation architecture that can efficiently learn from a fair amount of medical images to efficiently perform accurate segmentation tasks at low computing cost. Our model utilises a novel encoder-decoder architecture that incorporates an attention mechanism and GroupNormalizaton into a residual convolution block, allowing it to acquire and analyse image data at several resolutions within the encoder segment. On a benchmark set of myositis ultrasound pictures, the suggested method achieves state-of-the-art performance across all metrics including Precision, Recall, mean Dice coefficient, Jaccard index, and Accuracy. The proposed method exhibits high generalisation capabilities, producing outstanding results despite having access to a modest sample of training data. The proposed model achieved remarkable improvement in terms of accuracy compared to Unet++, DeepLabV3 and the original duck architecture which reached up to 12 %, 7 % and 2 % respectively. In addition, the proposed model also accomplished a noticeable improvement in terms of Dice Coefficient extended to 13 %, 11 % and 5 % respectively, when compared to the traditional models Unet++, DeepLab V3 and the original duck architecture respectively.

关键词： Measurement Image segmentation Accuracy Ultrasonic imaging Computational modeling Data visualization computer architecture

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Smart Facial Attendance Management System Using Machine Learning

Smart Facial Attendance Management System Using Machine Lear...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Harnish Joshi Nihar Bharatia Urmil Sakariya Dweepna Garg Parth Goel Department of Computer Engineering Faculty of Technology & Engineering (FTE) Devang Patel Institute of Advance Technology & Research (DEPSTAR) Charotar University of Science & Technology (CHARUSAT) Changa India Department of Computer Science and Engineering Faculty of Technology & Engineering (FTE) Devang Patel Institute of Advance Technology & Research (DEPSTAR) Charotar University of Science & Technology (CHARUSAT) Changa India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

Face recognition systems are essential in practically every industry in our digital age. One biometric that is frequently utilized is face recognition. It is helpful for security and has a ton of other advantages, identity, and authentication. Despite being less accurate than fingerprint and iris recognition, it is nonetheless commonly utilized because it is a non-invasive and contact-free technique. Additionally, facial recognition systems can be used to track attendance in workplaces, colleges, and schools, among other places. By solving problems with manual attendance procedures, like time consumption and the possibility of proxy attendance, this system seeks to develop an effective facial recognition-based classroom attendance system. As a result, this mechanism becomes more necessary. There are four components to this system capture face, train model, detect and recognize faces, update attendance record, and face detection. The photos of the children in the classroom are what make the data folder. The Haar-Cascade classifier and the Local Binary Pattern Histogram technique are used, respectively, for face detection and recognition. The suggested technique achieves approximately 80% accuracy when three to four students are present. Faces from live streaming video are identified and discovered. As a quick and safe substitute for manual attendance procedures, attendance is tracked in XLS sheets and forwarded to the appropriate faculty members at the conclusion of the session.

关键词： Industries Accuracy Face recognition Manuals Machine learning Streaming media Information age Face detection Security Iris recognition

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A Novel Approach for Predicting wide range of traffic congestion using deep learning Technique

A Novel Approach for Predicting wide range of traffic conges...

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Gnana Jeevan, A.N. Keerthika, K. Rao Terli, Sankara Naitik, S.T. Venkatesan, K.G.S. Manikandan, G. Seshasayee Institute of Technology Department of Computer Engineering Tamil Nadu Tiruchirappalli India Karpagam College of Engineering Department of Computer Science and Engineering Tamilnadu Coimbatore India Gitam Institute of Technology Department of Computer Science and Engineering Andhra Pradesh Visakhapatnam India KLS Gogte Institute of Technology Department of Computer Science and Engineering Karnataka India MEGHA Institute of Engg& Tech for Women Department of Computer Science and Engineering Telengana Hyderabad India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai Thandalam India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

Identifying traffic bottlenecks and devising solutions to the problem requires researchers and practitioners in transportation to understand how congestion in one place impacts the rest of the network. The dynamics of traffic congestion are often modelled using mathematical equations or simulation approaches. Most methods have flaws, such as unreliable assumptions and a time-consuming parameter calibration procedure. For example, ITS and the Internet of Things (IoT) have facilitated the collection of transportation data. This sets off a chain reaction of data-driven investigations into transportation *** learning theory is a potential technique for dealing with big, multidimensional datasets. For this research, deep learning theory will be used to analyze large-scale transportation networks. Recurrent Neural Networks and Restricted Boltzmann Machines are used to model and forecast the evolution of traffic congestion based on the GPS data from a cab. Numerical research was carried out in Delhi, India, to verify the method's efficacy and efficiency. Using a GPU-based parallel computing system, the forecast's accuracy might reach 88% in as little as six minutes. Congestion evolution trends are displayed using a map-based platform to identify weak links for proactive congestion reduction. © 2022 IEEE.

关键词： Traffic congestion

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Probabilistic Constellation Shaping for OFDM-Based ISAC Signaling

Probabilistic Constellation Shaping for OFDM-Based ISAC Sign...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Du, Zhen Liu, Fan Xiong, Yifeng Han, Tony Xiao Yuan, Weijie Cui, Yuanhao Yao, Changhua Eldar, Yonina C. School of Electronic and Information Engineering Nanjing University of Information Science & Technology Nanjing China Southern University of Science and Technology Department of Electronic and Electrical Engineering Shenzhen China School of Information and Electronic Engineering Beijing University of Posts and Telecommunications Beijing China Huawei Technologies Ltd. Shenzhen China Weizmann Institute of Science Faculty of Mathematics and Computer Science Rehovot Israel

ISBN: (纸本)9798350370218

Integrated Sensing and Communications (ISAC) has garnered significant attention as a promising technology for the upcoming sixth-generation wireless communication systems (6G). In pursuit of this goal, a common strategy is that a unified waveform, such as Orthogonal Frequency Division Multiplexing (OFDM), should serve dual-functional roles by enabling simultaneous sensing and communications (S&C) operations. However, the sensing performance of an OFDM communication signal is substantially affected by the randomness of the data symbols mapped from bit streams. Therefore, achieving a balance between preserving communication capability (i.e., the randomness) while improving sensing performance remains a challenging task. To cope with this issue, in this paper we analyze the ambiguity function of the OFDM communication signal modulated by random data. Subsequently, a probabilistic constellation shaping (PCS) method is proposed to devise the probability distributions of constellation points, which is able to strike a scalable S&C tradeoff of the random transmitted signal. Finally, the superiority of the proposed PCS method over conventional uniformly distributed constellations is validated through numerical simulations. © 2023 IEEE.

关键词： ambiguity function ISAC OFDM PCS

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AWS Server Less Luggage Tracking System Integrating IOT

AWS Server Less Luggage Tracking System Integrating IOT

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International Conference on Networking, Embedded and Wireless Systems (ICNEWS)

作者： Aditi Maharjan Prabhesh Yadav Chulindra Rai Ajay Kumar Mandal Basavaraju D.R Computer Science and Engineering- Internet of Things Faculty of Engineering & Technology’ JAIN (Deemed-to-be University) Bengaluru Karnataka India

ISBN: (数字)9798350367461

ISBN: (纸本)9798350367478

The global airline industry serves over two billion traveller annually and faces continuous challenges about luggage handling. Lost luggage, mishandling of luggage are most common problem for the traveller. With the number of traveller even small percentage of baggage mishandling translates to significant number of effected *** system examines the current implementation of Amazon Web Services (AWS) and Radio Frequency Identification (RFID) in in luggage handling and proposes the adoption of Internet of Things (IoT) technology to develop reusable luggage tags. The main goal of this paper is to explore the problems and limitations in existing luggage handling systems and propose and IoT-based solution to enhance the baggage tracking efficiency. Existing systems did not provide the facility to name the luggage in the application or end-user interfaceThe research focuses on three main goals: evaluating the effectiveness of current RFID and AWS-based systems, developing a reusable IoT-enabled luggage tag and also evaluating the impact on reducing mishandling of luggage. The proposed system integrates smart, reusable tags with IoT capabilities allowing real time tracking of the *** proposed system demonstrated that the IoT based luggage tracking system outperformed RFID systems in terms of tracking efficiency and accuracy. Overall, integrating IoT technology into luggage tracking systems provides a better solution to the challenges faced by the passenger of air travel. The system also reduces the changes of lost luggage incidents and promotes environmental sustainability by the use of reusable tags.

关键词： Wireless communication Web services Green products Control systems Real-time systems Internet of Things Servers Faces Radiofrequency identification Airline industry

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WANGCHANLION AND WANGCHANX MRC EVAL

arXiv

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arXiv 2024年

作者： Phatthiyaphaibun, Wannaphong Nonesung, Surapon Payoungkhamdee, Patomporn Limkonchotiwat, Peerat Udomcharoenchaikit, Can Sawatphol, Jitkapat Chaksangchaichot, Chompakorn Chuangsuwanich, Ekapol Nutanong, Sarana School of Information Science and Technology VISTEC Thailand Department of Computer Engineering Faculty of Engineering Chulalongkorn University Thailand

This technical report describes the development of WangchanLion, an instruction fine-tuned model focusing on Machine Reading Comprehension (MRC) in the Thai language. Our model is based on SEA-LION and a collection of instruction following datasets. To promote open research and reproducibility, we publicly release all training data, code, and the final model weights under the Apache-2 license. To assess the contextual understanding capability, we conducted extensive experimental studies using two Thai MRC datasets, XQuAD and Iapp_wiki_qa_squad. Experimental results demonstrate the model’s ability to comprehend the context and produce an answer faithful to the reference one in 0-shot and 1-shot settings. In addition, our evaluation goes beyond the traditional MRC. We propose a new evaluation scheme assessing the answer’s correctness, helpfulness, conciseness, and contextuality. Our code is available publicly at https://***/vistec-AI/WangchanLion. © 2024, CC BY-SA.

关键词： HTTP

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Assessing the Efficiency of AI for Multivariate Data Analysis 3

Assessing the Efficiency of AI for Multivariate Data Analysi...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Kumar, Jambi Ratna Raja Chaturvedi, Mukesh Anandhasilambarasan, D. Ashok, V. Nawadkar, Ashwini R. Va, Manoj Genba Sopanrao Moze College of Engineering Department of Computer Engineering Maharashtra Pune India Usha Martin University Faculty of Business Mangagement & Commerce Ranchi India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Mangalayatan University Department of Computer Science Aligarh India

ISBN: (纸本)9798350319125

This study aimed to evaluate the efficiency of synthetic Intelligence (AI) for multivariate records analysis. Multivariate statistics evaluation is a complicated venture that historically requires considerable manual labor and information in information science and machine mastering. AI can offer an extra green and accurate method to the venture. Consequently, this study employed a ramification of AI-based total methods to identify patterns and generate insights from the dataset. Traditional and ML algorithms were used to generate models based on the records. The models have been evaluated based on their metrics - accuracy, precision, and keep in mind. In the end, the effects from the models were analyzed to identify trends and institutions among the variables. The findings concluded that AI may want to provide a robust and accurate technique to perform multivariate records analysis. However, further research is needed to develop novel tactics to utilize AI for multivariate statistics evaluation with increased accuracy and precision. © 2023 IEEE.

关键词： considerable multivariate ramification statistics evaluation synthetic

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Discovery of dangerous self-medication methods with patients, by using social network mining

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International Journal of Business Intelligence and Data Mining 2023年第3期23卷 277-287页

作者： Samizadeh, Reza Zadeh, Morteza Khavanin Jadidi, Mahsa Rezapour, Mohammad Vatankhah, Sahar Technical & Engineering Faculty Alzahra University Tehran Iran Hasheminejad Kidney Center School of Medicine Iran University of Medical Sciences Tehran Iran Alzahra University Tehran Iran Iranian Ministry of Science Research and Technology Iran Tehran University of Medical Sciences Tehran Iran

Nowadays, social networks have replaced traditional media for information, and unfortunately, some people around the world, instead of reading books, turn to writings that are easily accessible on these networks. The present study categorises Persian texts on the Telegram social network at Jam Hospital and some Iranian websites on metabolic disease, obesity, and diabetes. Classifying data was done by text mining algorithms and the naive Bayes was more accurate than support vector machine. The results conclude that the 'Venustat' is one of the treatments that are emphasised by people, and they recommend this treatment to each other. In medical science, this drug has many complications, and it should not be used arbitrarily. Also a very dangerous drug namely 'Super Slim' is another drug that is strongly recommended by users. Therefore, raising public awareness is necessary to avoid relying on unscientific media content and facilitating access to medical services such as telemedicine. © 2023 Inderscience Enterprises Ltd.

关键词： Support vector machines

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