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Culture versus Policy: More Global Collaboration to Effectively Combat COVID-19

The Innovation 2020年第2期1卷 15-16页

作者： Jianping Li Kun Guo Enrique Herrera Viedma Heesoek Lee Jiming Liu Ning Zhong Luiz Flavio Autran Monteiro Gomes Florin Gheorghe Filip Shu-Cherng Fang Mujgan SagirÖzdemir Xiaohui Liu Guoqing Lu Yong Shi School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy&Data Science Chinese Academy of SciencesBeijing 100190China Department of Computer Science and Artificial Intelligence E.T.S.de Ingenieria Informatica y de TelecomunicacionesUniversity of Granada18071 GranadaSpain Department of Information Management Korea Advanced Institute of Science and TechnologySeoul 207-43 Korea Department of Computer Science and HKBU-CSD&NIPD Joint Research Laboratory for Intelligent Disease Surveillance and Control Hong Kong Baptist UniversityHong KongChina Department of Life Science and Informatics Maebashi Institute of TechnologyMaebashi 371-0816Japan Ibmec University Center Av.Presidente Wilson118Office#111020030-020 Rio de JaneiroBrazil The Romanian Academy Bucharest010071Romania Industrial and Systems Engineering Department North Carolina State UniversityRaleighNC 27695USA Department of Industrial Engineering Eskisehir Osmangazi University26480 EskisehirTurkey Department of Computer Science Brunel University LondonLondonUB83PHUK Department of Biology and School of Interdisciplinary Informatics University of Nebraska at OmahaOmahaNE 68182USA College of Information Science and Technology University of Nebraska at OmahaOmahaNE 68182USA

The outbreak of COVID-19 seriously challenges every government with regard to capacity and management of public health systems facing the catastrophic *** and anti-epidemic policy do not necessarily conflict with each *** countries and governments should be more tolerant to each other in seeking cultural and political consensus to overcome this historically tragic pandemic together.

关键词： seeking overcome policy

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Smart IoT CPO Storage Tank

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IOP Conference Series: Earth and Environmental science 2022年第1期998卷

作者： Rengga Arnalis Renjani Hermantoro Prima Okta Adi Nugraha Kartika Purwandari Teddy Suparyanto Bens Pardamean Department of Agricultural Engineering Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Indonesia's crude palm oil (CPO) production from year to year continues to increase, at the end of 2020 it reached 17.35 million tons, up 3.6% from the previous year. Increasing production will result in more CPO stock and require good storage. The storage process that occurs is to maintain the temperature of the CPO so that the quality is not damaged. This temperature regulation is still done manually and raises the risk of work accidents. The purpose of this research is to create a temperature control system and automatic volume measurement that can be monitored from a smartphone. The manufacture of a control system used ESP8266 NodeMCu microcontroller, temperature sensor, proximity sensor, and 1000-Watt heater. Programming used the Arduino IDE and C++. The result of this study was an IoT CPO Storage Tank design equipped with sensors and microcontrollers. The temperature was measured with the DS18B20 sensor had a data accuracy of 99.19% while the volume measured with the HC-SR04 sensor had an accuracy of 99.78%. data obtained from the sensor could be seen through the Thingspeak application from a computer or smartphone.

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Federated learning for crop yield prediction: A comprehensive review of techniques and applications

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MethodsX 2025年 14卷 103408页

作者： Hiremani, Vani Devadas, Raghavendra M. Preethi Sapna, R. Sowmya, T. Gujjar, Praveen Rani, N. Shobha Bhavya, K.R. Symbiosis Institute of Technology Symbiosis International (Deemed) University Pune India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Faculty of Management Studies JAIN (Deemed-to-be University) Bengaluru India MURTI Research Center Smart Agriculture Labs Department of Artificial Intelligence and Data Science GITAM School of Technology GITAM (Deemed to be) University Bengaluru India Department of Computer Science and Engineering GITAM School of Technology GITAM (Deemed to be) University Bengaluru India

The demand for food all over the world requires the implementation of advanced technologies to improve agricultural productivity. Federated Learning (FL) as a decentralized approach to machine learning facilitates collaborative model training on different data sources while maintaining privacy—making it highly applicable technology for sensitive agricultural data. This paper offers a systematic overview of the recent knowledge on the application of FL towards the prediction of crop yield and other agricultural uses. We discussed the mathematical basis of FL, the variety of machine learning models used, the types of used agricultural data, and the major performance metrics. The paper presents real-world applications and lists the current limitations, including communication overhead, data heterogeneity, and interpretability issues. Lastly, we introduce open research directions to inform the development of FL in precision agriculture. © 2025

关键词： Crop yield prediction data privacy Federated learning Machine learning Precision agriculture Rice cultivation

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Real-time Weed Identification Using Machine Learning and Image Processing in Oil Palm Plantations

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IOP Conference Series: Earth and Environmental science 2022年第1期998卷

作者： Erick Firmansyah Teddy Suparyanto Alam Ahmad Hidayat Bens Pardamean Agricultural Engineering Department Institute of Agricultural STIPER Yogyakarta 55282 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The effectiveness and efficiency of the operation of oil palm plantations are considered to be the most crucial factor to develop the productivity and profitability of the palm oil business. One of the major obstacles for the plants to optimally produce crops based on their capacity is caused by the presence of noxious weeds in the plantation area. However, weed control via chemical processes may potentially harm the surrounding environment if it is not properly managed. Therefore, an automatic system to assist the farmers to identify and control the weeds is required to minimize harmful impacts on the environment. Machine Learning (ML) and Artificial Intelligence (ai)-based systems provide powerful tools to perform such tasks. In this work, we aim for an ML-based system design to perform an automatic weed recognition task. The methodology can provide an effort for environmental sustainability in oil palm plantations. The weed identification involves the description, the local names, and tolerance class of the weeds as well as suggestions to control them. The flow of this work consists of weed and herbicide data acquisition, data labeling, model configurations, and data training. Further, the proposed system can be adopted as an android-based application in mobile devices that can deploy the trained model to predict weed category in both real-time and non-real-time tasks.

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Multiple Pseudo-Local Feedbacks for Controlling High-Order Oscillatory Objects 15

Multiple Pseudo-Local Feedbacks for Controlling High-Order O...

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15th International Scientific-Technical Conference on Actual Problems of Electronic Instrument engineering, APEIE 2021

作者： Zhmud, V.A. Nosek, J. Mansurova, M.E. Semibalamut, V.M. Fomin, Yu. N. Stukach, O.V. Novosibirsk State Technical University Institute of Laser Physics Sb Ras Dept. of Automation Novosibirsk Russia Technical University of Liberec Inst. of Mechatronics and Computer Engineering Liberec Czech Republic Al-Farabi Kazakh National University Department of Artificial Intelligence and Big Data Almaty Kazakhstan Unified Geophysical Service of the Russian Academy of Sciences Siberian Branch of the Federal State Budgetary Institution of Science of the Federal Research Center Novosibirsk Russia Novosibirsk State Technical University Higher School of Economics Dept. of Information Defense Moscow Russia

ISBN: (纸本)9781665434089

Development of the Internet of things (IoT) technologies arises many additional problems as energy harvesting. In particular, a long-Time controlling object needs a device without an external power supply. The control of technical objects with a high order of mathematical model has a large difficulty if a feature of the object is a high tendency to oscillations due to the ratio of the coefficients in the model. In particular, the tendency to oscillations occurs if the object model has complex roots, or if the model has a transport delay. One such example is a model that consists of two oscillatory objects and a delay loop. Widespread controlling such an objects using a PID controller is very difficult since in the transient process in the resulting system there is a large overshoot and many oscillations. If such an object could be stabilized with additional local feedbacks, the control task would be greatly simplified, but the object does not have corresponding outputs and inputs, therefore local feedbacks cannot be used. The pseudo-local feedback approach assumes the use of virtual fragments of the object's mathematical model in the controller to form such connections that the system perceives as local ones. This paper proposed the use of several pseudo-local feedbacks to control an object with elements that increased its tendency to oscillate. The effectiveness of the proposed approach is confirmed by modeling. © 2021 IEEE.

关键词： Feedback

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Experimental Realization of One Dimensional Helium

arXiv

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arXiv 2022年

作者： Del Maestro, Adrian Nichols, Nathan S. Prisk, Timothy R. Warren, Garfield Sokol, Paul E. Department of Physics and Astronomy University of Tennessee KnoxvilleTN37996 United States Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States Institute for Advanced Materials and Manufacturing University of Tennessee KnoxvilleTN37996 United States Data Science and Learning Division Argonne National Laboratory ArgonneIL60439 United States Center for Neutron Research National Institute of Standards and Technology GaithersburgMD20899-6100 United States Division of Chemistry and Chemical Engineering California Institute of Technology PasadenaCA91125 United States Department of Physics Indiana University BloomingtonIN47408 United States

The realization of experimental platforms exhibiting one dimensional (1D) quantum phenomena has been elusive, due to their inherent lack of stability, with a few notable exceptions including spin chains [1], carbon nanotubes [2] and ultracold low-density gasses [3]. The difficulty of such systems in exhibiting long range order is integral to their effective description in terms of the Tomonaga-Luttinger liquid theory [4-7]. Recently, it has been proposed that the bosonic superuid 4He could realize a 1D quantum system [8] beyond the Luttinger liquid paradigm [9]. Here we describe an experimental observation of this behavior using nanoengineering by preplating a porous material with a noble gas to enhance dimensional reduction. The resulting excitations of the confined 4He are qualitatively different than 3D and 2D superuid helium, and can be analyzed in terms of a mobile impurity in a Luttinger liquid [10] allowing for the characterization of the emergent quantum liquid. The confined helium system offers the possibility of tuning via pressure-from weakly interacting, all the way to the super Tonks-Girardeau gas of strongly interacting hardcore particles. Copyright © 2022, The Authors. All rights reserved.

关键词： Helium

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Generating 3D TOF-MRA volumes and segmentation labels using generative adversarial networks

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Medical Image Analysis 2022年 78卷 102396页

作者： Subramaniam, Pooja Kossen, Tabea Ritter, Kerstin Hennemuth, Anja Hildebrand, Kristian Hilbert, Adam Sobesky, Jan Livne, Michelle Galinovic, Ivana Khalil, Ahmed A. Fiebach, Jochen B. Frey, Dietmar Madai, Vince I. CLAIM - Charité Lab for AI in Medicine Charité Universitätsmedizin Berlin Germany Department of Computer Engineering and Microelectronics Computer Vision & Remote Sensing Technical University Berlin Berlin Germany Berlin Germany Bernstein Center for Computational Neuroscience Berlin Germany Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine Charité Universitätsmedizin Berlin Berlin Germany Fraunhofer MEVIS Max-von-Laue-Str. 2 Bremen Germany Department VI Computer Science and Media Beuth University of Applied Sciences Berlin Germany Johanna-Etienne-Hospital Neuss Germany Centre for Stroke Research Berlin Charité Universitätsmedizin Berlin Berlin Germany Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany Mind Brain Body Institute Berlin School of Mind and Brain Humboldt University Berlin Berlin Germany Berlin Institute of Health Berlin Germany School of Computing and Digital Technology Faculty of Computing Engineering and the Built Environment Birmingham City University Birmingham United Kingdom QUEST-Center for Transforming Biomedical Research Berlin Institute of Health Charité Universitätsmedizin Berlin Charitéplatz 1 Berlin10117 Germany

Deep learning requires large labeled datasets that are difficult to gather in medical imaging due to data privacy issues and time-consuming manual labeling. Generative Adversarial Networks (GANs) can alleviate these challenges enabling synthesis of shareable data. While 2D GANs have been used to generate 2D images with their corresponding labels, they cannot capture the volumetric information of 3D medical imaging. 3D GANs are more suitable for this and have been used to generate 3D volumes but not their corresponding labels. One reason might be that synthesizing 3D volumes is challenging owing to computational limitations. In this work, we present 3D GANs for the generation of 3D medical image volumes with corresponding labels applying mixed precision to alleviate computational constraints. We generated 3D Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) patches with their corresponding brain blood vessel segmentation labels. We used four variants of 3D Wasserstein GAN (WGAN) with: 1) gradient penalty (GP), 2) GP with spectral normalization (SN), 3) SN with mixed precision (SN-MP), and 4) SN-MP with double filters per layer (c-SN-MP). The generated patches were quantitatively evaluated using the Fréchet Inception Distance (FID) and Precision and Recall of Distributions (PRD). Further, 3D U-Nets were trained with patch-label pairs from different WGAN models and their performance was compared to the performance of a benchmark U-Net trained on real data. The segmentation performance of all U-Net models was assessed using Dice Similarity Coefficient (DSC) and balanced Average Hausdorff Distance (bAVD) for a) all vessels, and b) intracranial vessels only. Our results show that patches generated with WGAN models using mixed precision (SN-MP and c-SN-MP) yielded the lowest FID scores and the best PRD curves. Among the 3D U-Nets trained with synthetic patch-label pairs, c-SN-MP pairs achieved the highest DSC (0.841) and lowest bAVD (0.508) compared to the benchmark

关键词： Generative adversarial networks

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Utilization of fuzzy logic (FL) and whale optimization algorithm (WOA) for optimization of load scheduling in energy management systems

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International Journal of Information Technology (Singapore) 2025年 1-6页

作者： Suthar, Bhivraj Punia, Ankit Sharma, Prabhat Jain, Alok Narayan, Ram Raju, Y. Kamala Sharma, Nidhi Hussein, Ramy Riad Vafaeva, Khristina Maksudovna School of Artificial Intelligence and Data Science IIT Jodhpur Rajpura India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura 140417 India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh 174103 India Lovely Professional University Punjab Phagwara India Uttaranchal Institute of Technology Uttaranchal University Dehradun 248007 India Department of Computer Science GRIET Telangana Hyderabad India Department of Computer Engineering & Application GLA University Mathura India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Babylon Babylon Iraq Centre for Promotion of Research Graphic Era (Deemed to Be University) Dehradun 248002 India Peter the Great St. Petersburg Polytechnic University Saint Petersburg 195251 Russian Federation

Energy management is a crucial component of modern smart grid systems to ensure balance between supply-demand chains of power. The integration of complex structures for monitoring as well as existing techniques has enabled individual clients for actively participating in energy management programs. Thus, the given paper introduces an efficient approach to propose an energy management system for scheduling residential electricity loads. The proposed approach leverages fuzzy logic (FL) and whale optimization algorithm (WOA), incorporating consumption categories for operating household equipment during specific intervals. By adopting this strategy, the approach achieves significant reductions in peak electricity demand as well as lowers the electricity costs with regard to consumers. Additionally, the proposed approach attains the lowest mean average percentage error (MAPE %) and lowest power consumption (KWh) thereby surpassing existing studies in terms of performance. © Bharati Vidyapeeth's Institute of computer applications and Management 2025.

关键词： Energy management system FL Load scheduling Optimization WOA

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A real-time spatiotemporal ai model analyzes skill in open surgical videos

arXiv

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arXiv 2021年

作者： Goodman, Emmett D. Patel, Krishna K. Zhang, Yilun Locke, William Kennedy, Chris J. Mehrotra, Rohan Ren, Stephen Guan, Melody Downing, Maren Chen, Hao Wei Clark, Jevin Z. Brat, Gabriel A. Yeung, Serena Department of Computer Science Stanford University StanfordCA United States Department of Biomedical Data Science Stanford University StanfordCA United States Department of Electrical Engineering Stanford University StanfordCA United States Clinical Excellence Research Center Stanford University School of Medicine StanfordCA United States Department of Surgery Beth Israel Deaconess Medical Center BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States

Open procedures represent the dominant form of surgery worldwide. Artificial intelligence (ai) has the potential to optimize surgical practice and improve patient outcomes, but efforts have focused primarily on minimally invasive techniques. Our work overcomes existing data limitations for training ai models by curating, from YouTube, the largest dataset of open surgical videos to date: 1997 videos from 23 surgical procedures uploaded from 50 countries. Using this dataset, we developed a multi-task ai model capable of real-time understanding of surgical behaviors, hands, and tools-the building blocks of procedural flow and surgeon skill. We show that our model generalizes across diverse surgery types and environments. Illustrating this generalizability, we directly applied our YouTube-trained model to analyze open surgeries prospectively collected at an academic medical center and identified kinematic descriptors of surgical skill related to efficiency of hand motion. Our Annotated Videos of Open Surgery (AVOS) dataset and trained model will be made available for further development of surgical ai. Copyright © 2021, The Authors. All rights reserved.

关键词： computer vision

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Spatiotemporal Attention Autoencoder (STAAE) for ADHD Classification 23rd

Spatiotemporal Attention Autoencoder (STAAE) for ADHD Classi...

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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCai 2020

作者： Dong, Qinglin Qiang, Ning Lv, Jinglei Li, Xiang Liu, Tianming Li, Quanzheng Center for Advanced Medical Computing and Analysis Department of Radiology Massachusetts General Hospital and Harvard Medical School BostonMA United States School of Physics and Information Technology Shaanxi Normal University Xian China School of Biomedical Engineering and Sydney Imaging Brain and Mind Centre The University of Sydney Camperdown Australia Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center The University of Georgia AthensGA United States MGH & BWH Center for Clinical Data Science BostonMA United States

ISBN: (纸本)9783030597276

It has been of great interest in the neuroimaging community to model spatiotemporal brain function and disorders based on resting state functional magnetic resonance imaging (rfMRI). A variety of spatiotemporal methods have been proposed for rfMRI so far, including deep learning models such as convolution networks (CNN) and recurrent networks (RNN). However, the dominant models fail to capture the long-distance dependency (LDD) due to their sequential nature, which becomes critical at longer sequence lengths due to memory limit. Inspired by human brains extraordinary ability of long-term memory and attention, the attention mechanism is designed for machine translation to draw global dependencies and achieved state-of-the-art. In this paper, we propose a spatiotemporal attention autoencoder (STAAE) to discover global features that address LDDs in rfMRI. STAAE encodes the information throughout the rfMRI sequence and reveals resting state networks (RSNs) that characterize spatial and temporal properties of the data. Considering that the rfMRI is measured without external tasks, an unsupervised classification framework is developed based on the connectome generated with STAAE. This framework has been evaluated on 281 children with ADHD and 266 normal control children from 4 sites of ADHD200 datasets. The proposed STAAE reveals the global functional interaction in the brain and achieves a state-of-the-art classification accuracy from 59.5% to 77.2% on multiple sites. It is evident that the proposed attention-based model provides a novel approach towards better understanding of human brain. © 2020, Springer Nature Switzerland AG.

关键词： Brain

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