检索结果-内蒙古大学图书馆

Analysis of Knowledge Management System in Grabb Mobile Application Based On Usability

Journal of Physics: Conference Series 2020年第2期1477卷

作者： T. Thoyyibah Harco Leslie Hendric Spits Warnars Agung Trisetyarso Edi abdurachman Ford Lumban Gaol Benfano Soewito Computer Science Department Binus Graduate Program-Doctor of Computer ScienceBina Nusantara University Jl. Kebon Jeruk Raya No.27 South Tangerang Banten Faculty of Information Technology University Of Pamulang jl. Jl. Raya Puspitek Serpong No. 10 South Tangerang Banten

Grabb mobile application is a tool that is often used by people to travel. Each will travel then grabb will be ordered and pickup in front of the house and delivered to the destination location according to the address written on the application. Much knowledge can be disseminated through this Grabb application. Among them Grabbb car when going to go using a car, grabbike, service and others. The method in this study uses the adoption of a knowledge management system life cycle (KMSLC). Where the stages used are consists of Analysis of Existing Infrastructure Evaluation, Knowledge Capture, Implement the KM system, Evaluation. The steps taken are system analysis, knowledge mapping in the application, the results of implementation and evaluation using usability with 10 respondents through the adoption of Software Usability Measurement Inventory (SUMI). The results of SUMI's Usability are effectiveness with a value of 90, efficiency 70 and satisfaction 70. By looking at these results, the mobile grabb application is included in the good category because the value of usabililty is above average. A post-publication change was made to this article on 20 Apr 2020 as the previously published article was a duplicate.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Publisher Correction: The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles

引用

Genome biology 2025年第1期26卷 31页

作者： Jacob Matthew Schreiber Carles A Boix Jin Wook Lee Hongyang Li Yuanfang Guan Chun-Chieh Chang Jen-Chien Chang Alex Hawkins-Hooker Bernhard Schölkopf Gabriele Schweikert Mateo Rojas Carulla Arif Canakoglu Francesco Guzzo Luca Nanni Marco Masseroli Mark James Carman Pietro Pinoli Chenyang Hong Kevin Y Yip Jefrey P Spence Sanjit Singh Batra Yun S Song Shaun Mahony Zheng Zhang Wuwei Tan Yang Shen Yuanfei Sun Minyi Shi Jessika Adrian Richard S Sandstrom Nina P Farrell Jessica M Halow Kristen Lee Lixia Jiang Xinqiong Yang Charles B Epstein J Seth Strattan Bradley E Bernstein Michael P Snyder Manolis Kellis William S Noble Anshul Bharat Kundaje Department of Genetics Stanford University Stanford CA USA. jmschreiber91@***. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA. Department of Genetics Stanford University Stanford CA USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. Department of Research and Development DeepSeq.AI San Francisco CA USA. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Department of Empirical Inference Max Planck Institute for Intelligent Systems Stuttgart Germany. School of Life Sciences University of Dundee Dundee UK. Department of Electronics Information and Bioengineering Politecnico Di Milano Milan Italy. Department of Computational Biology University of Lausanne Lausanne Switzerland. Department of Computer Science and Engineering The Chinese University of Hong Kong Sha Tin Hong Kong. Sanford Burnham Prebys Medical Discovery Institute San Diego CA USA. Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA. Department of Statistics University of California Berkeley Berkeley CA USA. Department of Biochemistry & Molecular Biology Center for Eukaryotic Gene Regulation Pennsylvania State University University Park PA USA. Department of Statistics Pennsylvania State University University Park PA USA. Department of Electrical and Computer Engineering Texas A&M University College Station TX USA. Altius Institute Seattle WA USA. Epigenomics Program The Broad Institute of MIT and Harvard Cambridge MA USA. Department of Genome Sciences University of Washington Seattle WA USA. Department of Computer Science Stanford University Stanford CA USA.

来源：评论

学校读者我要写书评

暂无评论

Numerical analysis of general-vibration-based method for structural health monitoring

引用

AIP Conference Proceedings 2020年第1期2227卷

作者： Fergyanto E. Gunawan Benfano Soewito Nico Surantha Tuga Mauritsius Nobumasa Sekishita 1Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia 11480 2Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 3Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 4Department of Mechanical Engineering Toyohashi University of Technology Toyohashi Aichi 441-8580 Japan

For damage detection, this research article discusses an easy-to-compute damage index derived from the governing dynamic of the structure that has potential practical application in Structural Health Monitoring (SHM). The research uses simplified structural models to explore the sensitivity of the index to damages, to compare the index performance with a traditional but popular damage detection method, and to understand the local/global predictive capability of the index. The research uses two simple models, namely, single- and two-degree-of-freedom systems. The results suggest that the damage index is local, that can only monitor damages occurring near the points of measurements, but it is sensitive to damages, unlike the natural frequency, which is global but less sensitive.

关键词：

来源：评论

学校读者我要写书评

暂无评论

The accuracy of the F statistic for structural health monitoring

引用

AIP Conference Proceedings 2020年第1期2227卷

作者： Fergyanto E. Gunawan Benfano Soewito Nico Surantha Tuga Mauritsius Nobumasa Sekishita 1Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia 11480 2Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 3Information Systems Management Department BINUS Graduate Program – Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 4Department of Mechanical Engineering Toyohashi University of Technology Toyohashi Aichi 441-8580 Japan

Understanding the reliability of engineering methods is crucial for its adoption and deployment. This research focuses on the reliability of the Power Spectral Density (PSD) method via the use of the F statistic for damage detection. To the author best knowledge, the method is rather classic but its realibility has not been discussed in the context of a large data size. Priory, the research anticipates that the accuracy is a function of the damage level. In this study, we evaluate 3500 cases with five levels of structural integrity, namely, healthy condition and damaged conditions with 1%, 5%, 10%, and 20% damage levels. The dataset is established via a numerical analysis of a seven degree-of-freedom system loaded with a concentrated dynamic force with random magnitude. A spring on the system is reduced in its stiffness to simulate damages. Our significant findings are the following: it is challenging for the PSD-based method to differentiate the healthy condition from the damaged conditions when the damage level is small. However, the reliability is high at 95% probability when the structural integrity has dropped by five percent.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Expert System Diagnosis Corn Pests And Diseases Using Certainty Factor Method

引用

Journal of Physics: Conference Series 2019年第1期1230卷

作者： Sulindawaty Muhammad Zarlis Zakarias Situmorang Hengki Tamando Sihotang Doctoral Program Faculty of Computer Science and InformationTechnology Universitas Sumatera Utara Indonesia Faculty of Computer Science and Information Technology Universitas Sumatera Utara Indonesia

The development of technology has been very significant, not only in the fields of information, industry, education, but in agriculture. Therefore technological sophistication is also utilized by corn farmers to obtain information about corn crops. Corn is included in plantation commodities in Indonesia. However, in corn cultivation there are various problems associated with the disease that is likely to attack the plant. Therefore, the authors designed the expert system of corn disease diagnosis using certainty method. This system will analyze based on symptoms - symptoms that are in the input by the user so that in obtaining a result of disease identification on corn in the name of the disease, the cause of the disease, the percentage value and how to handle it. This expert system is developed by using the visual basic application and Microsoft Access database with Certainty Factor method to measure the value of certainty of a hypothesis against a fact.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition

引用

Database : the journal of biological databases and curation 2023年第2023期2023卷 baac108页

作者： Davy Weissenbacher Karen O'Connor Siddharth Rawal Yu Zhang Richard Tzong-Han Tsai Timothy Miller Dongfang Xu Carol Anderson Bo Liu Qing Han Jinfeng Zhang Igor Kulev Berkay Köprü Raul Rodriguez-Esteban Elif Ozkirimli Ammer Ayach Roland Roller Stephen Piccolo Peijin Han V G Vinod Vydiswaran Ramya Tekumalla Juan M Banda Parsa Bagherzadeh Sabine Bergler João F Silva Tiago Almeida Paloma Martinez Renzo Rivera-Zavala Chen-Kai Wang Hong-Jie Dai Luis Alberto Robles Hernandez Graciela Gonzalez-Hernandez Department of Computational Biomedicine Cedars-Sinai Medical Center Los Angeles CA USA. DBEI The Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd Zhongli District Taoyuan 320 Taiwan. IoX Center National Taiwan University Da'an District Section 4 Roosevelt Rd No. 1 Barry Lam Hall Taipei 106 Taiwan. Research Center for Humanities and Social Sciences Academia Sinica No. 128 Section 2 Academia Rd Nangang District Taipei 115 Taiwan. Computational Health Informatics Program Boston Children's Hospital Boston MA USA. Department of Pediatrics Harvard Medical School Boston MA USA. NVIDIA Santa Clara CA USA. Department of Statistics Florida State University Tallahassee FL USA. Data and Analytics Chapter F. Hoffmann-La Roche Ltd Switzerland. Pharmaceutical Research and Early Development Roche Innovation Center Basel Switzerland. Speech and Language Technology Lab DFKI Berlin Germany. Department of Biology Brigham Young University Provo UT USA. Department of Computational Medicine and Bioinformatics Medical School University of Michigan Ann Arbor MI USA. Department of Learning Health Sciences Medical School University of Michigan Ann Arbor MI USA. School of Information University of Michigan Ann Arbor MI USA. Department of Computer Science Georgia State University Atlanta GA USA. CLaC Labs Concordia University Montreal Canada. DETI Institute of Electronics and Informatics Engineering of Aveiro University of Aveiro Portugal. Department of Computation University of A Coruña Spain. Computer Science and Engineering Department Universidad Carlos III de Madrid Madrid Spain. Big Data Laboratory Chunghwa Telecom Laboratories Taoyuan Taiwan. Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan. Department of Electrical Engineering College of Electrical Engineering and Computer Sci

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user's timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user's timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://***/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.

关键词：

来源：评论

学校读者我要写书评

暂无评论

ThermalProGAN: A sequence-based thermally stable protein generator trained using unpaired data

引用

Journal of bioinformatics and computational biology 2023年第1期21卷 2350008页

作者： Hui-Ling Huang Chong-Heng Weng Torbjörn E M Nordling Yi-Fan Liou International Program of Health Informatics and Management College of Management Chang Gung University No. 259 Wenhua 1st Road Guishan District Taoyuan City 33302 Taiwan. Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Road Zhongli District Taoyuan City 320317 Taiwan. Department of Mechanical Engineering National Cheng Kung University No. 1 University Road Tainan City 701 Taiwan. Department of Applied Physics and Electronics Umeå University 90187 Umeå Sweden. Department of Virtual-Reality Interaction with Artificial Intelligence Technology Coretronic Reality Incorporation No. 5 Wenhua Rd. Hukou Township Hsinchu County 303 Taiwan.

MOTIVATION:The synthesis of proteins with novel desired properties is challenging but sought after by the industry and academia. The dominating approach is based on trial-and-error inducing point mutations, assisted by structural information or predictive models built with paired data that are difficult to collect. This study proposes a sequence-based unpaired-sample of novel protein inventor (SUNI) to build ThermalProGAN for generating thermally stable proteins based on sequence information. RESULTS:The ThermalProGAN can strongly mutate the input sequence with a median number of 32 residues. A known normal protein, 1RG0, was used to generate a thermally stable form by mutating 51 residues. After superimposing the two structures, high similarity is shown, indicating that the basic function would be conserved. Eighty four molecular dynamics simulation results of 1RG0 and the COVID-19 vaccine candidates with a total simulation time of 840[Formula: see text]ns indicate that the thermal stability increased. CONCLUSION:This proof of concept demonstrated that transfer of a desired protein property from one set of proteins is feasible. The source code of ThermalProGAN can be freely accessed at https://***/markliou/ThermalProGAN/ with an MIT license. The website is https://***:433. Supplementary data are available on Github.

关键词： CycleGAN Protein synthesis generative adversarial neural network thermal stability

来源：评论

学校读者我要写书评

暂无评论

Systematic literature review on corruption prevention efforts towards sustainable economy in Indonesia: ICT perspective 2

Systematic literature review on corruption prevention effort...

引用

2nd International Conference on Eco Engineering Development 2018, ICEED 2018

作者： Noerlina Kristin, D.M. Dewanti, R. Sasmoko Mursitama, T.N. Muqsith, A.M. Fajrianti, S.P. Krishti, N.S. Makalew, B.A. Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia Primary Teacher Education Department Faculty of Humanities Bina Nusantara University Jakarta11480 Indonesia Research Interest Group in Educational Technology Bina Nusantara University Jakarta11480 Indonesia International Relations Department Faculty of Humanities Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Psychology Department Faculty of Humanities Bina Nusantara University Jakarta11480 Indonesia Mobile Application and Technology Program School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Corruption is an extraordinary crime and must be eradicated not only as fast as possible, but also as efficient and as effective as possible as it hinders the process of achieving sustainable economy. In current modern society, information technology exists as a multidisciplinary tool to solve lingering problems that could not be solved by traditional efforts. This paper aims to provide knowledge regarding identified efforts to fight corruption specifically in Indonesia region. Systematic Literature Review (SLR) methodology is used as the methodology, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow is used as framework to identify relevant records. Scopus database is used as data source. A total of 10 records are labelled as relevant to this research, and the findings are synthesized. It was found from the records that the main areas of interest for ICT research for corruption prevention in Indonesia are (1) information transparency, (2) operational enhancement, (3) success evaluation, (4) security and fraud, and (4) decision support. The findings are expected to assist future research in term of providing valuable insights on corruption prevention using ICT in Indonesia. © 2018 Institute of Physics Publishing. All rights reserved.

关键词： Crime

来源：评论

学校读者我要写书评

暂无评论

Development of a Web Based Corruption Case Mapping Using Machine Learning with Artificial Neural Network 3

Development of a Web Based Corruption Case Mapping Using Mac...

引用

3rd International Conference on information Management and technology, ICIMTech 2018

作者： Noerlina Dewanti, Retno Mursitama, Tirta Nugraha Fairianti, Sheila Putri Kristin, Desi Maya Sasmoko Muqsith, Andi Muhammad Krishti, Nathasya Shesilia Makalew, Brillv Andro Information Systems Department Bina Nusantara University School of Information Systems Jakarta11480 Indonesia Management Department Bina Nusantara University BINUS Business School Undergraduate Program Jakarta11480 Indonesia International Relations Department Faculty of Humanities Bina Nusantara University Jakarta11480 Indonesia Psychology Department Faculty of Humanities Bina Nusantara University Jakarta11480 Indonesia Primary Teacher Education Department Faculty of Humanities Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Bina Nusantara University School of Computer Science Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Mobile Application Technology Program Jakarta11480 Indonesia

ISBN: (纸本)9781538658215

Cumulative state loss over the years caused by corruption in Indonesia has reached a fantastic number of 15 Billion usn up until 2016 [1]. To imagine the severances of it, 10.000 KM of highway can be built with that much amount of fund. Several responses has been done by Indonesian government to fight corruption from both prevention and persecution. This work focuses on the development of a web application aimed to provide insight to corruption case per province in Indonesia. The web application was developed using Machine Learning, specifically Backpropagation Artificial Neural Network (ANN). Web crawling and web scraping techniques are used to gather news content from 7 major news portal in Indonesia. Accuracy is measured by comparing correct prediction by ANN to its true value. Upon finding corruption news, data is saved and further analysis is done to establish the number of corruption news per region. Finally, the output is visualized using Google Map API. The purpose of this work is to provide regional depiction of corruption case to give further insight to be used by decision makers. Upon using this web application, objectivity of program development is expected to increase. The final output of the web application is a map of corruption case in Indonesia per region. The accuracy of ANN used in this work to classify corruption and non-corruption news is 96.91 % using Sigmoid Activation Function. © 2018 IEEE.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

Probing the universality of topological defect formation in a quantum annealer: Kibble-Zurek mechanism and beyond

引用

Physical Review Research 2020年第3期2卷 033369-033369页

作者： Yuki Bando Yuki Susa Hiroki Oshiyama Naokazu Shibata Masayuki Ohzeki Fernando Javier Gómez-Ruiz Daniel A. Lidar Sei Suzuki Adolfo del Campo Hidetoshi Nishimori Institute of Innovative Research Tokyo Institute of Technology Nagatsuta-cho Midori-ku Yokohama 226-8503 Japan Institute of Innovative Research Tokyo Institute of Technology Oh-okayama Meguro-ku Tokyo 152-8550 Japan Department of Physics Tohoku University Sendai 980-8578 Japan Graduate School of Information Sciences Tohoku University Sendai 980-8579 Japan Sigma-i Co. Ltd. Konan Minato-ku Tokyo 108-0075 Japan Donostia International Physics Center E-20018 San Sebastián Spain Departments of Electrical and Computer Engineering Chemistry and Physics University of Southern California Los Angeles California 90089 USA Center for Quantum Information Science & Technology University of Southern California Los Angeles California 90089 USA Department of Liberal Arts Saitama Medical University Moroyama Saitama 350-0495 Japan IKERBASQUE Basque Foundation for Science E-48013 Bilbao Spain Department of Physics University of Massachusetts Boston 100 Morrissey Boulevard Boston Massachusetts 02125 USA RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS) Wako Saitama 351-0198 Japan

The number of topological defects created in a system driven through a quantum phase transition exhibits a power-law scaling with the driving time. This universal scaling law is the key prediction of the Kibble-Zurek mechanism (KZM), and testing it using a hardware-based quantum simulator is a coveted goal of quantum information science. Here we provide such a test using quantum annealing. Specifically, we report on extensive experimental tests of topological defect formation via the one-dimensional transverse-field Ising model on two different D-Wave quantum annealing devices. We find that the quantum simulator results can indeed be explained by the KZM for open-system quantum dynamics with phase-flip errors, with certain quantitative deviations from the theory likely caused by factors such as random control errors and transient effects. In addition, we probe physics beyond the KZM by identifying signatures of universality in the distribution and cumulants of the number of kinks and their decay, and again find agreement with the quantum simulator results. This implies that the theoretical predictions of the generalized KZM theory, which assumes isolation from the environment, applies beyond its original scope to an open system. We support this result by extensive numerical computations. To check whether an alternative, classical interpretation of these results is possible, we used the spin-vector Monte Carlo model, a candidate classical description of the D-Wave device. We find that the degree of agreement with the experimental data from the D-Wave annealing devices is better for the KZM, a quantum theory, than for the classical spin-vector Monte Carlo model, thus favoring a quantum description of the device. Our work provides an experimental test of quantum critical dynamics in an open quantum system, and paves the way to new directions in quantum simulation experiments.

关键词： Critical exponents Nonequilibrium statistical mechanics Open quantum systems Quantum computation Quantum master equation Quantum simulation Quantum statistical mechanics Quantum spin models

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：