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Chemical Electron Tomography at Lower Dose and Higher Resolution

Microscopy and Microanalysis 2024年第SUPPLEMENT_1期30卷

作者： Hovden, Robert Schwartz, Jonathan Sung, Suk Hyun Di, Zichao Wendy Jiang, Yi Manassa, Jason Pietryga, Jacob Qian, Yiwen Cho, Min Gee Rowell, Jonathan L Zheng, Huihuo Robinson, Richard D Gu, Junsi Kirilin, Alexey Rozeveld, Steve Ercius, Peter Scott, Mary Department of Materials Science and Engineering University of Michigan Ann Arbor MI Applied Physics Program University of Michigan Ann Arbor MI Chan Zuckerberg Initiative Redwood City CA Rowland Institute at Harvard Cambridge MA. Mathematics and Computer Science Division Argonne National Laboratory Lemont IL Advanced Photon Source Facility Argonne National Laboratory Lemont IL Department of Materials Science and Engineering U.C. Berkeley Berkeley CA National Center for Electron Microscopy Molecular Foundry Berkeley CA Department of Chemistry and Chemical Biology Cornell University Ithaca NY Argonne Leadership Computing Facility Argonne National Laboratory Lemont IL Dow Chemical Co. Terneuzen Netherlands Dow Chemical Co. Midland MI

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Identification of types of tree damage based on forest health monitoring (FHM) in the management of TAHURA WAR utilization blocks

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AIP Conference Proceedings 2024年第1期2970卷

作者： Rahmat Safe’i Rico Andrian Tri Maryono Sepia Tapasya Tri Rizky Putra Master of Forestry Study Program Forestry Department Faculty of Agriculture University of Lampung Jl. Prof. Dr. Ir. Soemantri Brodjonegoro No. 1 Bandar Lampung Lampung 35145 Indonesia Dept. Computer Science Faculty of Mathematics and Natural Science University of Lampung Jl. Prof. Dr. Ir. Soemantri Brodjonegoro No. 1 Bandar Lampung Lampung 35145 Indonesia Plant Protection Study Program Faculty of Agriculture University of Lampung Jl. Prof. Dr. Ir. Soemantri Brodjonegoro No. 1 Bandar Lampung Lampung 35145 Indonesia Departement of Forestry Faculty of Agriculture University of Lampung Jl. Prof. Dr. Ir. Soemantri Brodjonegoro No. 1 Bandar Lampung Lampung 35145 Indonesia

Regarding management, TAHURA WAR is divided into blocks, one of which is the utilization block. In this block, the community can carry out utilization and use of forest areas. One of the causes of forest use by the community in this block is damage to trees due to high human activities or other concomitant causes. This study aimed to identify the damage to trees based on FHM in the TAHURA WAR utilization block. This research was conducted on five FHM cluster plots of utilization blocks that have been built. Identification is made by measuring and analyzing trees’ location, type, and severity based on the FHM method. The results showed 14 varieties of tree damage from 345 trees. The most common types of damage found were open wounds (code 03) with 162 trees and cancer (code 01) with 78 trees with an average severity of 20-29%. The two types of tree damage were often found at the location of the lower bole (code 3) and lower and upper bole (code 4), with the status of the level of tree damage in the moderate category (6.7 – 9.9). Thus, in the TAHURA WAR utilization block, two dominant types of tree damage were identified. This condition resulted in a considerable decrease in the quantity and quality of wood in the TAHURA WAR utilization block.

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Industrial revolution 4.0 on big data adoption factors on social media users and popularity brands

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Journal of Physics: Conference Series 2021年第1期1839卷

作者： W Sardjono G Rama Putra E Selviyanti A Cholidin G Salim Information Systems Management Department BINUS Graduate Program – Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 Computer Science Study Program Faculty of Mathematics & Natural Sciences Pakuan University Bogor Indonesia Health Department Politeknik Negeri Jember Jember Jawa Timur Indonesia 68124 Law Studi program Faculty of Law University of Muhammadiyah Jakarta Indonesia Post Graduate Program Faculty of Economic Science University of Trisakti Jakarta Indonesia

The purpose of writing this paper is to look at how factors of big data adoption affect the users of social media and brand popularity. The method used in the writing of this paper is by using path analysis and also by doing literature study to find reference material. The results of the analysis indicate that there is influence of big data adoption factors that affect the popularity of the brand. With this research, we get big data model that influence the brand popularity.

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Implementation of Fuzzy-based Model for Prediction of Thalassemia Diseases

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Journal of Physics: Conference Series 2021年第1期1751卷

作者： E. R. Susanto A. Syarif K Muludi R. R. W. Perdani A. Wantoro Doctoral Program of Mathematics and Science Faculty Lampung University. Jl. Sumantri Brojonegoro No 1 Bandar Lampung Indonesia. Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia. Jl. ZA. Pagaralam No.9-11 Bandar Lampung Indonesia. Department of Computer Science Faculty of Mathematics and Science University of Lampung. Jl. Sumantri Brojonegoro No 1 Bandar Lampung Indonesia. Faculty of Medical Science Lampung University. Jl. Sumantri Brojonegoro No 1 Bandar Lampung Indonesia

Thalassemia is known as one of the blood disorder diseases that is inherited by parents. There are several types of Thalassemia, namely as Thalassemia major, minor, and intermedia. Among them, Thalassemia major is the most dangerous and needs more attention. Generally, it can be detected since the child is one year old. Late detection of this disease can have adverse consequences and various complications. This study aims to develop a new model for the prediction of thalassemia for children. The model adopts a fuzzy-based rule. The novelty in this article is that our model has 4 outputs, namely thalassemia major, intermedia, minor and not thalassemia. In the previous article it only had 3 outputs. In this study, we intend to implement a model that we developed using a fuzzy-based approach to classify thalassemia diseases based on CBC data. This article describes how to build a model and implement it in software. We compare the test results with the opinion of pediatricians regarding thalassemia. The final results of testing 4 CBC data show that our proposed model has successfully identified the type of thalassemia.

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Expert System Application of Forward Chaining and Certainty Factors Method for the Decision of Contraception Tools 2

Expert System Application of Forward Chaining and Certainty ...

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2nd International Conference on Energy, Environmental and Information System, ICENIS 2017

作者： Prambudi, Dwi Arief Widodo, Catur Edi Widodo, Aris Puji Magister Program of Information System School of Postgraduate Studies Diponegoro University Semarang50242 Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang50275 Indonesia Department of Computer Science Faculty of Science and Mathematics Diponegoro University Semarang50275 Indonesia

The choice of contraceptive tools is not an easy thing because the risks or effects will give impact on the body that never using it previously. in the other side, there is no contraception always suit for everybody because the circumstances of each body is different, so the extensive knowledge must be needed to know the advantages and disadvantages of each contraceptive tools then adjusted to the user's *** expert system for contraceptive tools uses Forward Chaining search method combined with Certainty Factors Method. These method value the patient's indication. The Expert system gives the output data which define the kind of tool uses of the patient. the results obtained will be able to help people to find indications that lead to appropriate contraceptive tools and advice or suggestions about these tools. The success rate of the contraceptive tools decision experienced by experienced by the user by using forward chaining combined with the CF computation method is also influenced by the number of indication criteria selected by the user. Based on testing that has been done, expert system contraception tools has accuracy level equal to 75%. © 2018 The Authors, published by EDP sciences.

关键词： Expert systems

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A meta-analysis of Boolean network models reveals design principles of gene regulatory networks

arXiv

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arXiv 2020年

作者： Kadelka, Claus Butrie, Taras-Michael Hilton, Evan Kinseth, Jack Schmidt, Addison Serdarevic, Haris Department of Mathematics Iowa State University AmesIA50011 United States Department of Aerospace Engineering Iowa State University AmesIA50011 United States Department of Computer Science Iowa State University AmesIA50011 United States Bioinformatics and Computational Biology Program Iowa State University AmesIA50011 United States

Gene regulatory networks (GRNs) play a central role in cellular decision-making. Understanding their structure and how it impacts their dynamics constitutes thus a fundamental biological question. GRNs are frequently modeled as Boolean networks, which are intuitive, simple to describe, and can yield qualitative results even when data is sparse. We assembled the largest repository of expert-curated Boolean GRN models. A meta-analysis of this diverse set of models reveals several design principles. GRNs exhibit more canalization, redundancy and stable dynamics than expected. Moreover, they are enriched for certain recurring network motifs. This raises the important question why evolution favors these design mechanisms. Copyright © 2020, The Authors. All rights reserved.

关键词： Decision making

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Will artificial intelligence supersede earth system and climate models?

arXiv

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arXiv 2021年

作者： Irrgang, Christopher Boers, Niklas Sonnewald, Maike Barnes, Elizabeth A. Kadow, Christopher Staneva, Joanna Saynisch-Wagner, Jan Helmholtz Centre Potsdam German Research Centre for Geosciences GFZ Potsdam Germany Department of Mathematics and Computer Science Free University of Berlin Germany Potsdam Institute for Climate Impact Research Potsdam Germany Department of Mathematics and Global Systems Institute University of Exeter United Kingdom Program in Atmospheric and Oceanic Sciences Princeton University PrincetonNJ08540 United States NOAA OAR Geophysical Fluid Dynamics Laboratory Ocean and Cryosphere Division PrincetonNJ08540 United States University of Washington School of Oceanography SeattleWA United States Colorado State University Fort Collins United States German Climate Computing Center DKRZ Hamburg Germany Helmholtz-Zentrum Geesthacht Center for Material and Coastal Research HZG Geesthacht Germany

We outline a perspective of an entirely new research branch in Earth and climate sciences, where deep neural networks and Earth system models are dismantled as individual methodological approaches and reassembled as learning, self-validating, and interpretable Earth system model-network hybrids. Following this path, we coin the term"Neural Earth System Modelling" (NESYM) and highlight the necessity of a transdisciplinary discussion platform, bringing together Earth and climate scientists, big data analysts, and AI experts. We examine the concurrent potential and pitfalls of Neural Earth System Modelling and discuss the open question whether artificial intelligence will not only infuse Earth system modelling, but ultimately render them obsolete. © 2021, CC BY.

关键词： Climate models

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Perceptron Partition Model to Minimize Input Matrix

Perceptron Partition Model to Minimize Input Matrix

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International Conference on science and Innovated Engineering 2018, i-COSINE 2018

作者： Azmi, Zulfian Nasution, Mahyuddin K.M. Zarlis, Muhammad. Mawengkang, Herman Efendi, Syahril Postgraduate Doctoral of Computer Science Universitas Sumatera Utara Padang Bulan USU Medan20155 Indonesia Information Technology Department Universitas Sumatera Utara Padang Bulan USU Medan20155 Indonesia Program Pasca Sarjana Doktoral Ilmu Komputer Universitas Sumatera Utara Indonesia Department of Mathematics Universitas Sumatera Utara Padang BulanUSU Medan20155 Indonesia

Implementation of Neuron Network model using Perceptron has not given optimal result in real time learning. The large number of inputs expressed in matrix form makes the process slower in pattern recognition. So, it takes characteristic to represent all the input matrices by using the partition method. By partitioning each input and with the best weight and using the activation function will produce an output value. And learning is valid in recognizing patterns only 1 iteration only. Further validation is done on the water mill control module with dissolved oxygen input, water pH, salinity and water temperature. With Perceptron Partition learning algorithm more real-time than perceptron model. Testing on the waterwheel input whether rotating or stopping by Matrix Laboratory software simulation. © Published under licence by IOP Publishing Ltd.

关键词： Neural networks

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Visualizing structure and transitions in high-dimensional biological data (vol 54, pg 781, 2019)

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NATURE BIOTECHNOLOGY 2020年第1期38卷 108-108页

作者： Moon, Kevin R. van Dijk, David Wang, Zheng Gigante, Scott Burkhardt, Daniel B. Chen, William S. Yim, Kristina van den Elzen, Antonia Hirn, Matthew J. Coifman, Ronald R. Ivanova, Natalia B. Wolf, Guy Krishnaswamy, Smita Department of Mathematics and Statistics Utah State University Logan USA Cardiovascular Research Center section Cardiology Department of Internal Medicine Yale University New Haven USA Department of Computer Science Yale University New Haven USA School of Basic Medicine Qingdao University Qingdao China Yale Stem Cell Center Department of Genetics Yale University New Haven USA Computational Biology and Bioinformatics Program Yale University New Haven USA Department of Genetics Yale University New Haven USA Department of Computational Mathematics Science and Engineering Michigan State University East Lansing USA Department of Mathematics Michigan State University East Lansing USA Applied Mathematics Program Yale University New Haven USA Department of Genetics Center for Molecular Medicine University of Georgia Athens USA Department of Mathematics and Statistics Université de Montréal Montréal Canada Mila—Quebec Artificial Intelligence Institute Montréal Canada

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Fast Communication-efficient Spectral Clustering Over Distributed Data

arXiv

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arXiv 2019年

作者： Yan, Donghui Wang, Yingjie Wang, Jin Wu, Guodong Wang, Honggang Department of Mathematics and Program in Data Science Department of Electrical and Computer Engineering University of Massachusetts DartmouthMA United States Lovelace Respiratory Research Institute AlbuquerqueNM United States

The last decades have seen a surge of interests in distributed computing thanks to advances in clustered computing and big data technology. Existing distributed algorithms typically assume all the data are already in one place, and divide the data and conquer on multiple machines. However, it is increasingly often that the data are located at a number of distributed sites, and one wishes to compute over all the data with low communication overhead. For spectral clustering, we propose a novel framework that enables its computation over such distributed data, with "minimal" communications while a major speedup in computation. The loss in accuracy is negligible compared to the non-distributed setting. Our approach allows local parallel computing at where the data are located, thus turns the distributed nature of the data into a blessing;the speedup is most substantial when the data are evenly distributed across sites. Experiments on synthetic and large UC Irvine datasets show almost no loss in accuracy with our approach while about 2x speedup under various settings with two distributed sites. As the transmitted data need not be in their original form, our framework readily addresses the privacy concern for data sharing in distributed computing. Copyright © 2019, The Authors. All rights reserved.

关键词： Large dataset

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