检索结果-内蒙古大学图书馆

Nature Biotechnology 2025年

作者： Zhang, Feifei Chow, Ryan D. He, Emily Dong, Chuanpeng Xin, Shan Mirza, Daniyal Feng, Yanzhi Tian, Xiaolong Verma, Nipun Majety, Medha Zhang, Yueqi Wang, Guangchuan Chen, Sidi Department of Genetics Yale University School of Medicine New HavenCT United States System Biology Institute Yale University West HavenCT United States Center for Cancer Systems Biology Yale University West HavenCT United States M.D.–Ph.D. Program Yale University West HavenCT United States Molecular Cell Biology Genetics and Development Program Yale University New HavenCT United States Yale College New HavenCT United States Immunobiology Program Yale University New HavenCT United States Department of Therapeutic Radiology Yale University School of Medicine New HavenCT United States Department of Neurosurgery Yale University School of Medicine New HavenCT United States Yale Comprehensive Cancer Center Yale University School of Medicine New HavenCT United States Yale Stem Cell Center Yale University School of Medicine New HavenCT United States Yale Center for Biomedical Data Science Yale University School of Medicine New HavenCT United States Key Laboratory of Multi-Cell Systems Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences Shanghai China

Correction to: Nature Biotechnologyhttps://***/10.1038/s41587-024-02535-2, published online 16 January 2025. In the version of the article initially published, Sidi Chen was incorrectly associated with a present address. This has now been corrected in the HTML and PDF versions of the article. © The Author(s), under exclusive licence to Springer Nature America, Inc. 2025.

关键词：

A growth model for water distribution networks with loops

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Sugishita, Kashin Abdel-Mottaleb, Noha Zhang, Qiong Masuda, Naoki Department of Mathematics State University of New York at Buffalo BuffaloNY14260-2900 United States Department of Transdisciplinary Science and Engineering Tokyo Institute of Technology Tokyo152-8550 Japan Department of Civil and Environmental Engineering University of South Florida TampaFL33620 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States Faculty of Science and Engineering Waseda University Tokyo169-8555 Japan

Water distribution networks (WDNs) expand their service areas over time. These growth dynamics are poorly understood. One facet of WDNs is that they have loops in general, and closing loops may be a functionally important process for enhancing their robustness and efficiency. We propose a growth model for WDNs which generates networks with loops and is applicable to networks with multiple water sources. We apply the proposed model to four empirical WDNs to show that it produces networks whose structure is similar to that of the empirical WDNs. The comparison between the empirical and modeled WDNs suggests that the empirical WDNs may realize a reasonable balance between cost, efficiency, and robustness in terms of the network structure. We also study the design of pipe diameters based on a biological positive feedback mechanism. Specifically, we apply a model inspired by Physarum polycephalum to find moderate positive correlations between the empirical and modeled pipe diameters. The difference between the empirical and modeled pipe diameters suggests that we may be able to improve the performance of WDNs by following organizing principles of biological flow networks. Copyright © 2021, The Authors. All rights reserved.

关键词： Water distribution systems

Smart IoT CPO Storage Tank

学校读者我要写书评

暂无评论

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.

关键词：

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

学校读者我要写书评

暂无评论

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.

关键词：

Deeplenstronomy: A dataset simulation package for strong gravitational lensing

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Morgan, Robert Nord, Brian Birrer, Simon Lin, Joshua Yao-Yu Poh, Jason University of Wisconsin-Madison United States Legacy Survey of Space and Time Data Science Fellowship Program United States Fermi National Accelerator Laboratory United States University of Chicago United States Stanford University United States University of Illinois Urbana-Champaign United States

deeplenstronomy generates SL datasets by organizing and expediting user interaction with lenstronomy. The user creates a single yaml-style configuration file that describes the aspects of the dataset: number of images, properties of the telescope and camera, cosmological parameters, observing conditions, properties of the physical objects, and geometry of the SL systems. deeplenstronomy parses the configuration file and generates the dataset, producing both the images and the parameters that led to the production of each image as outputs. The configuration files can easily be shared, enabling users to easily reproduce each other's training datasets. The premier objective of deeplenstronomy is to help astronomers make their training datasets as realistic as possible. To that end, deeplenstronomy contains built-in features for the following functionalities: use any stellar light profile or mass profile in lenstronomy;simulate a variety of astronomical systems such as single galaxies, foreground stars, galaxy clusters, supernovae, and kilonovae, as well as any combination of those systems;fully control the placements of objects in the simulations;use observing conditions of real astronomical surveys;draw any parameter from any probability distribution;introduce any correlation;and incorporate real images into the simulation. Furthermore, deeplens tronomy facilitates realistic time-domain studies by providing access to public spectral energy distributions of observed supernovae and kilonovae and incorporating the transient objects into time series of simulated images. Finally, deeplenstronomy provides data visualization functions to enable users to inspect their simulation outputs. These features and the path from configuration file to full data set are shown in Figure 1. deeplenstronomy makes use of multiple open-source software packages: lenstronomy is used for all gravitational lensing calculations and image simulation;numpy (Harris et al. 2020) Arrays are used inter

关键词： Supernovae

3D Liver and Tumor Segmentation in CT Scans with Robust Self-Paced Learning

学校读者我要写书评

暂无评论

3D Liver and Tumor Segmentation in CT Scans with Robust Self...

International Conference on Information science and Control Engineering (ICISCE)

作者： Obed Tettey Nartey Guowu Yang Sarpong Kwadwo Asare Jinzhao Wu Dorothy Araba Yakoba Agyapong Lady Nadia Frempong Big Data Research Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Mathematics and Physics Guangxi University for Nationalities Nanning China Biomedical Engineering Program Kwame Nkrumah University of Science and Technology Kumasi Ghana

Liver segmentation is a challenging problem where fully supervised deep learning models require large amounts of voxel-wise labels, which are usually laborious, expensive, and time consuming to obtain. However, massive non-annotated 3D CT volumes are usually easily accessible. Most deep learning based works to address semi-supervised CT medical image segmentation problems have mostly been graph-based or adversarial-based. In sharp contrast, this work investigates the potential of a semi-supervised learning method; novel self-training framework utilizing self-paced learning strategy to generate and select pseudo-labels from easy-to-transfer(well represented classes) to hard-to-transfer(less represented classes) samples. The proposed scheme introduces an effective pseudo-label generation and selection algorithm to generate highly confident pseudo-labels, jointly learn and optimize a model which can be solved by alternatively generating pseudo labels on unlabeled target data and re-training the model with the generated labels. Further, we also propose a novel class balancing framework to avoid the gradual eminence of easy-to-transfer discriminative features on pseudo-label generation utilizing class-wise normalization of confidence scores. Comprehensive experiments conducted on publicly available datasets show that the proposed method outperformed other state-of-the-art semi-supervised and some fully supervised methods on the segmentation results of tumors and achieved very competitive performance for liver segmentation.

关键词： Training Deep learning Image segmentation Information science Three-dimensional displays Computed tomography Liver

Stress Level Classifier: Taiwanese College Table Tennis Athletes' Electroencephalography Analysis Based on Decision Trees 1

学校读者我要写书评

暂无评论

Stress Level Classifier: Taiwanese College Table Tennis Athl...

1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020

作者： Cheng, Ping-Sung Tsai, Meng-Hsiun Hsueh, Chung-Hao Kuang Wu, Sheng National Chung Hsing University Department of Management Information Systems Taichung City Taiwan National Chung Hsing University AI and Data Science Program Institute of Genomic and Boioinformatics Department of Management Information Systems Taichung City Taiwan National Taiwan University of Sport Department of Sport Performance Taichung City Taiwan

ISBN: (纸本)9781665404839

This study aims to provide a method to quantify the stress level with numerical EEG values, identify key features of brainwave and assess the level of stress for table tennis players. The data of College's Division 1 and Division 2 are collected and analyzed by the decision tree algorithms C4.5, CART, Random Forest and Random Tree. The result of Random Forest obtains the highest accuracy rate among each algorithm, which is 79.21% in all players, 79.3% in Division 1, and 80.68% in Division 2. According to the result of decision trees, the top attribute of the Division 1 players was Theta wave, which was different from the result of other data in the Division 2 players. Also reveal the deference of brainwaves between the Division 2 players and the Division 1 players while they are in high stressed competitions. © 2020 IEEE.

关键词： Electroencephalography

Finite-size effects on the convergence time in continuous-opinion dynamics

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Jo, Hang-Hyun Masuda, Naoki Department of Physics The Catholic University of Korea Bucheon14662 Korea Republic of Department of Mathematics State University of New York at Buffalo 14260-2900 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo 14260-5030 United States

We study finite-size effects on the convergence time in a continuous-opinion dynamics model. In the model, each individual's opinion is represented by a real number on a finite interval, e.g., [0;1], and a uniformly randomly chosen individual updates its opinion by partially mimicking the opinion of a uniformly randomly chosen neighbor. We numerically find that the characteristic time to the convergence increases as the system size increases according to a particular functional form in the case of lattice networks. In contrast, unless the individuals perfectly copy the opinion of their neighbors in each opinion updating, the convergence time is approximately independent of the system size in the case of regular random graphs, uncorrelated scale-free networks, and complete graphs. We also provide a mean-field analysis of the model to understand the case of the complete graph. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph theory

*** - A plug-and-play solution for online systems medicine and network-based drug repurposing

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Maier, Andreas Hartung, Michael Abovsky, Mark Adamowicz, Klaudia Bader, Gary D. Baier, Sylvie Blumenthal, David B. Chen, Jing Elkjaer, Maria L. Garcia-Hernandez, Carlos Helmy, Mohamed Hoffmann, Markus Jurisica, Igor Kotlyar, Max Lazareva, Olga Levi, Hagai List, Markus Lobentanzer, Sebastian Loscalzo, Joseph Malod-Dognin, Noel Manz, Quirin Matschinske, Julian Mee, Miles Oubounyt, Mhaned Pico, Alexander R. Pillich, Rudolf T. Poschenrieder, Julian M. Pratt, Dexter Pržulj, Nataša Sadegh, Sepideh Saez-Rodriguez, Julio Sarkar, Suryadipto Shaked, Gideon Shamir, Ron Trummer, Nico Turhan, Ugur Wang, Ruisheng Zolotareva, Olga Baumbach, Jan Institute for Computational Systems Biology University of Hamburg Hamburg Germany Division of Orthopaedic Surgery Schroeder Arthritis Institute Data Science Discovery Centre Osteoarthritis Research Program Krembil Research Institute UHN Toronto Canada Data Science Discovery Centre for Chronic Diseases Krembil Research Institute University Health Network 60 Leonard Avenue 5KD-407 TorontoONM5T 0S8 Canada Department of Molecular Genetics University of Toronto TorontoON Canada The Donnelly Centre University of Toronto TorontoON Canada Department of Computer Science University of Toronto TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada The Lunenfeld-Tanenbaum Research Institute Mount Sinai Hospital TorontoON Canada TUM School of Life Sciences Technical University of Munich Munich Germany Erlangen91052 Germany Department of Medicine University of California San Diego 9500 Gilman Drive La Jolla CA92093 United States Department of Neurology Odense University Hospital Odense Denmark Institute of Clinical Research University of Southern Denmark Odense Denmark Institute of Molecular Medicine University of Southern Denmark Odense Denmark Barcelona08034 Spain Institute for Advanced Study Lichtenbergstrasse 2a GarchingD-85748 Germany Technical University of Munich Germany National Institute of Diabetes Digestive and Kidney Diseases BethesdaMD20892 United States Departments of Medical Biophysics and Computer Science University of Toronto Toronto Canada Institute of Neuroimmunology Slovak Academy of Sciences Bratislava Slovakia Heidelberg69120 Germany Heidelberg Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg69117 Germany Blavatnik School of Computer Science Tel-Aviv University Tel-Aviv Israel Heidelberg University Faculty of Medicine Heidelberg University Hospital Institute for Computational Biomedicine Bioquant Heidelberg Germany Department of Medicine Bri

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce ***, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, *** turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, *** has been successfully integrated with 21 computational systems medicine tools. Available at https://***, *** has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research. © 2023, CC BY.

关键词： data visualization

A search for H i absorption in distant star-forming galaxies with ASKAP-FLASH - I. Selection and analysis of the radio sample

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Eden, Sophie L. Sadler, Elaine M. Pimbblet, Kevin A. Mahony, Elizabeth K. Yoon, Hyein E. A. Milne Centre for Astrophysics University of Hull Cottingham Road Kingston-upon-HullHU6 7RX United Kingdom Sydney Institute for Astronomy School of Physics A28 University of Sydney SydneyNSW2006 Australia ATNF CSIRO Space and Astronomy PO Box 76 EppingNSW1710 Australia Australia Institute for Data Innovation in Science Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Astronomy Program Department of Physics and Astronomy Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of

We present and discuss two catalogues of UV-selected (NUV 2 area of sky covered by the ASKAP FLASH survey and have an impact parameter of less than 20 arcsec to a FLASH radio continuum source. These catalogues are designed to enable a future search for 21 cm H i absorption in and around star-forming galaxies at redshift 0.4 80 percent) star-forming and resemble the overall WiggleZ population. The (g − i) colour evolution with redshift is consistent with a history of active star-formation, but the radio-associated objects are typically redder and have colours similar to high-excitation radio galaxies. The redshift distribution of the two catalogues matches the overall distribution for WiggleZ galaxies, despite their otherwise rare radio properties. These catalogues can be expanded in future as new radio data become available, and a forthcoming paper will present the H i absorption results. © 2024, CC BY.

关键词： Stars