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A dynamic platform for global pullorum disease and fowl typhoid

Animal Diseases 2025年第1期5卷 113-123页

作者： Xiamei Kang Canghong Jin Pengkun Gu Song Wang Zhengjie Gao Chenyi Dai Xiao Zhou Abubakar Siddique Haiyang Zhou Linlin Huang Yan Li Min Yue Department of Veterinary Medicine Zhejiang University College of Animal SciencesHangzhou 310058China School of Computer and Computing Science Hangzhou City UniversityHangzhou 310015China Hainan Institute of Zhejiang University Sanya 572000China School of Life Science Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China State Key Laboratory for Diagnosis and Treatment of Infectious Diseases National Clinical Research Center for Infectious DiseasesNational Medical Center for Infectious DiseasesThe First Affiliated HospitalCollege of MedicineZhejiang UniversityHangzhou 310003China Ningbo Academy of Agricultural Sciences Ningbo 315040China NewFit(Hangzhou)Information Technology Co. LtdHangzhou 310000China

Pullorum disease and fowl typhoid are caused by the Salmonella serovars Gallinarum biovars Pullorum and Gal-linarum,*** prevalence of these diseases varies across regions and is affected by different risk fac-tors that remain insufficiently *** fill this knowledge gap,we have compiled a global dataset for its prevalence,drawing upon a collection of literature from the last seven decades obtained from bilingual ***,a more interactive and dynamic platform is still needed for both academics and policymakers to improve biosecurity measures,limit disease transmission,and prevent future outbreaks at the global and local ***,we developed an advanced visualization platform to depict the prevalence of Salmonella Pullorum and Gallinarum,espe-cially in China,which is categorized by geographical region and temporal *** platform offers a user-friendly,efficient,and visually engaging tool to explore the prevalence of pullorum disease and fowl typhoid between 1945 and 2021 in different ***,this platform allows users to understand the influence of various fac-tors,such as breed,farm mode,economic usage and even the sex of the primary host,chickens,on the prevalence of this *** further provided a detailed overview of individual province within *** particular,by selecting two different provinces on the interface,users can quickly visualize and grasp the disparities in disease prevalence between the chosen *** interactive toolkit enables a dynamic exploration of the patterns and factors con-tributing to the prevalence of Salmonella Pullorum and *** interactive platform is freely available open source at http://139.9.85.208/.

关键词： Pullorum disease Fowl typhoid Interactive platform Prevalence prediction

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Intelligent Breast Cancer Prediction Empowered with Fusion and Deep Learning

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computers, Materials & Continua 2021年第4期67卷 1033-1049页

作者： Shahan Yamin Siddiqui Iftikhar Naseer Muhammad Adnan Khan Muhammad Faheem Mushtaq Rizwan Ali Naqvi Dildar Hussain Amir Haider School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan School of Computer Science Minhaj University LahoreLahore54000Pakistan Department of Computer Science&Information Technology Superior UniversityLahore54000Pakistan Department of Computer Science Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Information Technology Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan Department of Unmanned Vehicle Engineering Sejong UniversitySeoul05006Korea School of Computational Sciences Korea Institute for Advanced Study(KIAS)85 HoegiroDongdaemunguSeoul02455Korea Department of Intelligent Mechatronics Engineering Sejong UniversitySeoul05006Korea

Breast cancer is the most frequently detected tumor that eventually could result in a significant increase in female mortality *** to clinical statistics,one woman out of eight is under the threat of breast *** and inheritance patterns may be a reason behind its spread among ***,some preventive measures,such as tests and periodic clinical checks can mitigate its risk thereby,improving its survival chances *** diagnosis and initial stage treatment can help increase the survival *** that purpose,pathologists can gather support from nondestructive and efficient computer-aided diagnosis(CAD)*** study explores the breast cancer CAD method relying on multimodal medical imaging and decision-based *** multimodal medical imaging fusion,a deep learning approach is applied,obtaining 97.5%accuracy with a 2.5%miss rate for breast cancer prediction.A deep extreme learning machine technique applied on feature-based data provided a 97.41%***,decisionbased fusion applied to both breast cancer prediction models to diagnose its stages,resulted in an overall accuracy of 97.97%.The proposed system model provides more accurate results compared with other state-of-the-art approaches,rapidly diagnosing breast cancer to decrease its mortality rate.

关键词： Fusion feature breast cancer prediction deep learning convolutional neural network multi-modal medical image fusion decision-based fusion

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Joint Optimization of Sensing and Communications in Vehicular Networks: A Graph Neural Network-Based Approach

Joint Optimization of Sensing and Communications in Vehicula...

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IEEE International Conference on Communications (ICC)

作者： Xuefei Li Mingzhe Chen Zhilong Zhang Danpu Liu Yuchen Liu Shiwen Mao Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Computer Science North Carolina State University Raleigh NC USA Department of Electrical and Computer Engineering Auburn University Auburn AL USA

In this paper, the problem of joint sensing and communications is studied over terahertz (THz) vehicular networks. In the studied model, a set of service provider vehicles provide either communication service or sensing service to communication target vehicles or sensing target vehicles, respectively. Therefore, it is necessary to determine the service mode (i.e., providing sensing or communication service) for each service provider vehicle and the subset of target vehicles that each service provider vehicle will serve. The problem is formulated as an optimization problem aiming to maximize the sum of the data rates of all communication target vehicles while satisfying the sensing service requirements of all sensing target vehicles by determining the service mode and the user association for each service provider vehicle. To solve this problem, a graph neural network (GNN) based algorithm with a heterogeneous graph representation is proposed. The proposed algorithm enables the central controller to extract each vehicle's graph information related to its location, connection, and communication interference. Using the extracted graph information, the joint service mode selection and user association strategy will be determined. Simulation results show that the proposed GNN-based scheme can achieve 94% of the sum rate produced by the optimal solution, and yield up to 3.95% and 36.16% improvements in sum rate, respectively, compared to a homogeneous GNN-based algorithm and the conventional optimization algorithm without using GNNs.

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Experience and Analysis of Scalable High-Fidelity Computational Fluid Dynamics on Modular Supercomputing Architectures

arXiv

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arXiv 2024年

作者： Karp, Martin Suarez, Estela Meinke, Jan H. Andersson, Måns I. Schlatter, Philipp Markidis, Stefano Jansson, Niclas Department of Computer Science KTH Royal Institute of Technology Stockholm Sweden Jülich Supercomputing Centre Institute for Advanced Simulations Forschungszentrum Jülich GmbH Jülich Germany Institute of Computer Science University of Bonn Bonn Germany Erlangen-Nürnberg Germany SimEx/FLOW Engineering Mechanics KTH Royal Institute of Technology Stockholm Sweden PDC Centre for High Performance Computing KTH Royal Institute of Technology Stockholm Sweden

The never-ending computational demand from simulations of turbulence makes computational fluid dynamics (CFD) a prime application use case for current and future exascale systems. High-order finite element methods, such as the spectral element method, have been gaining traction as they offer high performance on both multicore CPUs and modern GPU-based accelerators. In this work, we assess how high-fidelity CFD using the spectral element method can exploit the modular supercomputing architecture at scale through domain partitioning, where the computational domain is split between a Booster module powered by GPUs and a Cluster module with conventional CPU nodes. We investigate several different flow cases and computer systems based on the Modular Supercomputing Architecture (MSA). We observe that for our simulations, the communication overhead and load balancing issues incurred by incorporating different computing architectures are seldom worthwhile, especially when I/O is also considered, but when the simulation at hand requires more than the combined global memory on the GPUs, utilizing additional CPUs to increase the available memory can be fruitful. We support our results with a simple performance model to assess when running across modules might be beneficial. As MSA is becoming more widespread and efforts to increase system utilization are growing more important our results give insight into when and how a monolithic application can utilize and spread out to more than one module and obtain a faster time to solution. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational fluid dynamics

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IoT based Monitoring of Mushroom

IoT based Monitoring of Mushroom

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Sustainable computing and Data Communication Systems (ICSCDS), International Conference on

作者： J. Lysa Eben Chamandeep Kaur Mathews T Thelly Parimita Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Chennai India Department of Computer Science and Information Technology Jazan University Jazan Saudi Arabia Department of Botany Kuriakose Elias College Mannanam India Agricultural Engineering Sam Higginbottom University of Agriculture Technology & Sciences Prayagraj India

Most of India’s population relies on farming for their livelihood. Every year brings new advances in agricultural technology. The use of wireless sensors in contemporary agriculture is crucial. The employment of wireless sensors across a wide range of agricultural applications has a profoundly favourable effect on crop productivity and economic efficiency. The mushroom business is still relatively young and modest compared to other sectors of India’s agricultural market. The white button mushroom is India’s most commercially significant fungus and is a global favourite. It may be grown anywhere with a suitable climate and soil, but most of the world’s supply comes from North India in the winter. For mycelia to flourish, the ideal temperature range is between 22 and 25 degrees Celsius. In contrast, the sweet spot for developing fruit bodies is between 14 and 18 degrees Celsius with high relative humidity. Equipment for maintaining a steady temperature and pasteurising the mushrooms, should be present in the rooms used for growing. The primary objective of this study is to automate the mushroom production plant and keep track of the crop room’s environmental state to reduce the amount of human care required for the mushroom plant. The Internet of Things has also modernized agriculture industries, creating an environmental monitoring and regulating system that is used in the mushroom farm in this work. Using the Speak internet platform, users may monitor ecological conditions in a mushroom farm on their Android mobile, including the ambient temperature, relative humidity, carbon dioxide level, and light intensity. The Arduino prototype platform is employed as the sensors station’s controller, ensuring that the plant parameters are within the user-specified range. Using a pair of low-power ESP8266 as Wi-Fi modems, the current parameter status is relayed to a distant monitoring station. The integrated Arduino uses the programming environment to write the controller’s cod

关键词： Temperature measurement Temperature sensors Wireless communication Temperature distribution Wireless sensor networks Soil moisture Crops

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Issues in the Ethics of Using Generative AI

Issues in the Ethics of Using Generative AI

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International Conference on Cognitive Infocommunications (CogInfoCom)

作者： Carl Vogel Anna Esposito Trinity Centre for Computing and Language Studies School of Computer Science and Statistics Trinity College Dublin The University of Dublin Department of Psychology Università Degli Studi Della Campania Luigi Vanvitelli Caserta Italy and International Institute for Advanced Scientific Studies (IIASS) Italy

ISBN: (数字)9798350378245

ISBN: (纸本)9798350378252

Reflections on the relationship between performance enhancing drugs and some forms of academic dishonesty are presented. Generative AI is analyzed as a cognitive infocommunications technology, a technology intended in part to enhance human cognitive capabilities. Research intending to develop and improve generative AI is argued to be ethically sound. The use of generative AI as an enhancement to human cognitive capabilities in direct competitive scenarios such as assessment in education settings and the indirect competition of knowledge production is found to be lacking in ethical merit. The purpose of this article is to stimulate debate by adopting and defending firm, if extreme, positions on the ethical questions at hand.

关键词： Hands Drugs Ethics Generative AI Education Ecosystems Production Reflection Time measurement Socioeconomics

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Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5520-5537页

作者： Jian Liu Wei Sun Hui Yang Pengchao Deng Chongpei Liu Nicu Sebe Hossein Rahmani Ajmal Mian National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics and the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China Department of Information Engineering and Computer Science University of Trento Trento Italy School of Computing and Communications Lancaster University Lancaster U.K. Department of Computer Science The University of Western Australia Crawley WA Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance.

关键词： Shape Solid modeling Three-dimensional displays Pose estimation Training Robots Grasping Diffusion models Real-time systems Point cloud compression

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m6A-TSHub:Unveiling the Context-specific m^(6)A Methylation and m^(6)A-affecting Mutations in 23 Human Tissues

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Genomics, Proteomics & Bioinformatics 2023年第4期21卷 678-694页

作者： Bowen Song Daiyun Huang Yuxin Zhang Zhen Wei Jionglong Su João Pedro de Magalhães Daniel J.Rigden Jia Meng Kunqi Chen Key Laboratory of Gastrointestinal Cancer(Fujian Medical University) Ministry of EducationSchool of Basic Medical SciencesFujian Medical UniversityFuzhou 350004China Department of Mathematical Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou 215123China Institute of Systems Molecular and Integrative BiologyUniversity of LiverpoolLiverpool L697ZBUnited Kingdom Department of Biological Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou 215123China Department of Computer Science University of LiverpoolLiverpool L697ZBUnited Kingdom Institute of Ageing&Chronic Disease University of LiverpoolLiverpool L697ZBUnited Kingdom School of AI and Advanced Computing Xi’an Jiaotong-Liverpool UniversitySuzhou 215123China AI University Research Centre Xi’an Jiaotong-Liverpool UniversitySuzhou 215123China

As the most pervasive epigenetic marker present on mRNAs and long non-coding RNAs(lncRNAs),N6-methyladenosine(m^(6)A)RNA methylation has been shown to participate in essential biological *** studies have revealed the distinct patterns of m^(6)A methylome across human tissues,and a major challenge remains in elucidating the tissue-specific presence and circuitry of m^(6)A *** present here a comprehensive online platform,m^(6)A-TSHub,for unveiling the context-specific m^(6)A methylation and genetic mutations that potentially regulate m^(6)A epigenetic mark.m^(6)A-TSHub consists of four core components,including(1)m^(6)A-TSDB,a comprehensive database of 184,554 functionally annotated m^(6)A sites derived from 23 human tissues and 499,369 m^(6)A sites from 25 tumor conditions,respectively;(2)m^(6)A-TSFinder,a web server for high-accuracy prediction of m^(6)A methylation sites within a specific tissue from RNA sequences,which was constructed using multi-instance deep neural networks with gated attention;(3)m^(6)ATSVar,a web server for assessing the impact of genetic variants on tissue-specific m^(6)A RNA modifications;and(4)m^(6)A-CAVar,a database of 587,983 The Cancer Genome Atlas(TCGA)cancer mutations(derived from 27 cancer types)that were predicted to affect m^(6)A modifications in the primary tissue of *** database should make a useful resource for studying the m^(6)A methylome and the genetic factors of epitranscriptome disturbance in a specific tissue(or cancer type).m^(6)A-TSHub is accessible at ***/biologicalsciences/m^(6)ats.

关键词： N^(6)-methyladenosine Context-specific analysis Cancer mutation Genome analysis Functional annotation

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Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

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computers, Materials & Continua 2022年第9期72卷 5117-5134页

作者： Hiam Alquran Wan Azani Mustafa Isam Abu Qasmieh Yasmeen Mohd Yacob Mohammed Alsalatie Yazan Al-Issa Ali Mohammad Alqudah Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid22110Jordan Department of Biomedical Systems and Informatics Engineering Yarmouk University 556Irbid21163Jordan Faculty of Electrical Engineering Technology Campus Pauh PutraUniversiti Malaysia Perlis02000ArauPerlisMalaysia Advanced Computing Centre of Excellence(CoE)Universiti Malaysia Perlis(UniMAP)02000ArauPerlisMalaysia The Institute of Biomedical Technology King Hussein Medical CenterRoyal Jordanian Medical ServiceAmman11855Jordan Department of Computer Engineering Yarmouk UniversityIrbid21163Jordan

Cervical cancer is screened by pap smear methodology for detection and classification *** smear images of the cervical region are employed to detect and classify the abnormality of cervical *** this paper,we proposed the first system that it ables to classify the pap smear images into a seven classes *** smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images *** features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine(SVM)*** success of this proposed system in distinguishing between the levels of normal cases with 100%accuracy and 100%*** top of that,it can distinguish between normal and abnormal cases with an accuracy of 100%.The high level of abnormality is then studied and classified with a high *** the other hand,the low level of abnormality is studied separately and classified into two classes,mild and moderate dysplasia,with∼92%*** proposed system is a built-in cascading manner with five models of polynomial(SVM)*** overall accuracy in training for all cases is 100%,while the overall test for all seven classes is around 92%in the test phase and overall accuracy reaches 97.3%.The proposed system facilitates the process of detection and classification of cervical cells in pap smear images and leads to early diagnosis of cervical cancer,which may lead to an increase in the survival rate in women.

关键词： Classification deep learning machine learning pap smear images resnet101 support vector machines

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A Machine Learning Approach for Predictive Analysis of Jasmine Flower Yield and Plant Health Monitoring

A Machine Learning Approach for Predictive Analysis of Jasmi...

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Electronic Systems and Intelligent computing (ICESIC), International Conference on

作者： P. Malathi S. Rukmani Devi K. Senbagam T. Thirumalaikumari H. Anwer Basha B. Thamizhkani Department of Computer Science Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science and Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram Chennai Tamil Nadu India Department of Information Technology School of Computing Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Tamil Nadu India Department of EEE New Prince Shri Bhavani College of Engineering and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522995

Plant diseases and unpredictable yields are two issues that jasmine flower production must deal with, necessitating cutting-edge solutions for effective crop management. It provides a hybrid approach for plant health monitoring and jasmine yield prediction based on machine learning. For yield prediction, a Random Forest algorithm is used, and for illness identification via picture analysis, a Convolutional Neural Network (CNN) is deployed. For realtime monitoring, leaf photos and data from IoT sensors such as temperature, humidity, and soil moisture are analyzed. The suggested model outperforms conventional models in terms of accuracy, with a Mean Absolute Error (MAE) of 7.3 and an accuracy of 89% for yield prediction. CNN's F1-score for identifying plant diseases was 90.5%. With the help of TensorFlow and MATLAB Simulink, the system is implemented and offers farmers useful information. This all-inclusive solution improves crop management efficiency in jasmine production by optimizing yield projections and early disease detection.

关键词： Temperature sensors Plant diseases Accuracy Production Predictive models Flowering plants Mathematical models Convolutional neural networks Monitoring Random forests

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