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The document provides information about ceramics, including:
1. It lists the 11 group members of the ceramic group and provides a brief history of ceramics dating back to 24,000 BC.
2. It describes the main compositions of clay which are feldspar minerals comprising 60% of the earth's crust. Clays are divided into primary and secondary types.
3. The manufacturing process of ceramics includes various molding techniques like injection molding and isostatic pressing. Characteristics of ceramics include high hardness, resistance to chemicals and temperatures up to 2400属C.
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Ceramics (classification)
Clay products employed in building industry.
Tiles
Terracotta
Earthenware
Stoneware
Porcelain
Bricks
Manufacturing process of common tiles.
Types of common tiles used in building industries.
Characteristics of good tiles
The document discusses the Angel of the North sculpture in Gateshead, UK. It provides background on its creation, funding, and unveiling. It also discusses the artist Antony Gormley's vision for the sculpture and how it has become a symbol and part of the identity of the North East region over time. The document then analyzes efforts to measure the social and economic impacts of public art projects and whether they achieve their aims.
This document discusses extracting protein interactions from biomedical literature. It notes that while databases contain some known interactions, much information remains in unstructured text across millions of published articles. The document presents an example Medline abstract describing interactions between cyclin A, cyclin D1, CDK subunits, and the Rb protein. It aims to develop automated methods to locate and structure such interaction information at scale from literature.
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This document summarizes a request for proposal from the Missouri Office of Administration for janitorial services in various state-owned buildings in St. Louis. It provides information on submitting proposals, including revising the return date to April 14, 2010. It outlines amendments made to sections of the RFP related to the contract period, specifications, and pricing. A pre-proposal conference was scheduled for March 30, 2010 to discuss the RFP and allow for questions.
This document presents a system for classifying brain MRI series using decision tree learning. The system performs classification in two levels: 1) low-level features are used to classify segmented images into objects, and 2) high-level features synthesized from the low-level results are used to classify the full MRI series. Experiments classified MRI series as normal, cerebral infarction, or brain tumor with 93.1% accuracy. The two-level approach allows both low-level image features and high-level semantic relationships to be leveraged for classification.
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The document provides an agenda and information for the 2009 NIB/NAEPB Annual Training Conference held in Kansas City, Missouri from October 21-24, 2009. The conference focused on technology and strategic impacts with sessions addressing business topics to help agency leaders and employees. Activities included keynote speakers, committee meetings, tours of Alphapointe Association for the Blind, and an awards banquet honoring employee award winners. Presentations and materials from the conference were made available online.
The document provides an overview of two approaches to enabling computers to understand the content of the World Wide Web: (1) the structuring solution, which aims to impose structure on web pages similar to databases, and (2) the learning solution, which aims to make computers smarter through machine learning. It then summarizes three examples of researchers pursuing the learning solution, including using naive Bayes and maximum entropy classifiers to categorize web pages. The researchers were able to achieve 60% accuracy for page classification using naive Bayes but saw improved performance from maximum entropy and adding additional heuristics.
The document discusses machine learning techniques for finding patterns in data and using those patterns to make predictions. It covers topics like classification algorithms, decision trees, neural networks, learning as a search process, and how machine learning systems use bias to avoid overfitting training data. Examples are provided on classifying weather data to determine if a baseball game should be played, classifying iris flowers, predicting CPU performance, and diagnosing soybean diseases.
This document provides a summary of Mohak Shah's education, research interests, and professional experience. It summarizes that Mohak Shah is a researcher in machine learning and natural language processing at the University of Ottawa with a PhD in Computer Science. His research interests include machine learning theory, applications in NLP and bioinformatics. He has published extensively in refereed conferences and journals.
The document is a curriculum vitae for Colin Fyfe. It summarizes that he is currently a Personal Professor at the University of the West of Scotland, with educational qualifications including a BSc in Mathematics, MSc in Information Technology, and a PhD in neural networks. It also outlines his extensive employment history in education and research, as well as his significant research contributions and roles in academic administration and conference organization.
Knowledge mining is a process of extracting patterns and relationships from databases to improve decision-making. It has various applications including business intelligence, product design, manufacturing, and research profiling. The document then provides three examples: 1) A financial company used knowledge mining to analyze customer data and target marketing promotions. 2) Engineers have used it to help design products by matching requirements to existing part designs. 3) Researchers have used text mining to map relationships between topics in academic literature and identify active individuals and organizations.
The document discusses data mining and knowledge discovery in databases. It defines data mining as the nontrivial extraction of implicit and potentially useful information from large amounts of data. With huge increases in data collection and storage, data mining aims to analyze data and discover patterns that can provide insights and knowledge about businesses and the real world. The data mining process involves selecting, preprocessing, transforming, and analyzing data to extract hidden patterns and relationships, which are then interpreted and evaluated.
This document presents a model called SMART (Sustainable Model for Assimilating Research and Teaching) that was developed to integrate current machine learning research topics into the undergraduate curriculum at the University of Central Florida. The model involves faculty co-teaching courses on machine learning research topics and then having students participate in hands-on research projects advised by the faculty. Over 15 undergraduates per year are exposed to research through this approach. The model has continued operating after initial funding ended, demonstrating its sustainability.
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In this work,there may be some requirements of finding out the coupling loss factors of system component.It becomes difficult to exactly know the coupling loss factor by looking at the behavior of the system. For this purpose, the numerical solution developed in this work. Initially, one need to extract the displacement, velocity and energy profiles of the system which has got the components installed for which the coupling loss factor need to be determined. Then the numerical simulations can be run for different coupling loss factor of the vibrating system and the coupling loss factor can be found when the simulation results match with the experimental measurements. In this paper the experimentation is carried out i for the model a)Pre-design application of the work developed. b) Post design application of the work developed. The numerical results converge very well towards the experimental results as the coupling loss factor in simulation is varied towards the actual value. Similarly, for the second approach the experimental results converge towards the simulation results of 0.15 as the coupling loss factor of the damper that is installed on the system is varied towards 0.15.
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This course covers statistical methods for data mining and machine learning, including linear regression and classification, nonparametric methods, generalized additive models, and tree-based methods. Students will complete homework assignments, a midterm exam, and a final project analyzing a dataset of their choice. The project involves formulating a research question, applying course techniques to answer it, and presenting results in a written report. Evaluation is based on homework, exams, and the final project. The course uses the R programming environment.
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The document provides an agenda and information for the 2009 NIB/NAEPB Annual Training Conference held in Kansas City, Missouri from October 21-24, 2009. The conference focused on technology and strategic impacts with sessions addressing business topics to help agency leaders and employees. Activities included keynote speakers, committee meetings, tours of Alphapointe Association for the Blind, and an awards banquet honoring employee award winners. Presentations and materials from the conference were made available online.
The document provides an overview of two approaches to enabling computers to understand the content of the World Wide Web: (1) the structuring solution, which aims to impose structure on web pages similar to databases, and (2) the learning solution, which aims to make computers smarter through machine learning. It then summarizes three examples of researchers pursuing the learning solution, including using naive Bayes and maximum entropy classifiers to categorize web pages. The researchers were able to achieve 60% accuracy for page classification using naive Bayes but saw improved performance from maximum entropy and adding additional heuristics.
The document discusses machine learning techniques for finding patterns in data and using those patterns to make predictions. It covers topics like classification algorithms, decision trees, neural networks, learning as a search process, and how machine learning systems use bias to avoid overfitting training data. Examples are provided on classifying weather data to determine if a baseball game should be played, classifying iris flowers, predicting CPU performance, and diagnosing soybean diseases.
This document provides a summary of Mohak Shah's education, research interests, and professional experience. It summarizes that Mohak Shah is a researcher in machine learning and natural language processing at the University of Ottawa with a PhD in Computer Science. His research interests include machine learning theory, applications in NLP and bioinformatics. He has published extensively in refereed conferences and journals.
The document is a curriculum vitae for Colin Fyfe. It summarizes that he is currently a Personal Professor at the University of the West of Scotland, with educational qualifications including a BSc in Mathematics, MSc in Information Technology, and a PhD in neural networks. It also outlines his extensive employment history in education and research, as well as his significant research contributions and roles in academic administration and conference organization.
Knowledge mining is a process of extracting patterns and relationships from databases to improve decision-making. It has various applications including business intelligence, product design, manufacturing, and research profiling. The document then provides three examples: 1) A financial company used knowledge mining to analyze customer data and target marketing promotions. 2) Engineers have used it to help design products by matching requirements to existing part designs. 3) Researchers have used text mining to map relationships between topics in academic literature and identify active individuals and organizations.
The document discusses data mining and knowledge discovery in databases. It defines data mining as the nontrivial extraction of implicit and potentially useful information from large amounts of data. With huge increases in data collection and storage, data mining aims to analyze data and discover patterns that can provide insights and knowledge about businesses and the real world. The data mining process involves selecting, preprocessing, transforming, and analyzing data to extract hidden patterns and relationships, which are then interpreted and evaluated.
This document presents a model called SMART (Sustainable Model for Assimilating Research and Teaching) that was developed to integrate current machine learning research topics into the undergraduate curriculum at the University of Central Florida. The model involves faculty co-teaching courses on machine learning research topics and then having students participate in hands-on research projects advised by the faculty. Over 15 undergraduates per year are exposed to research through this approach. The model has continued operating after initial funding ended, demonstrating its sustainability.
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The document provides guidance for completing a form to set up a new programme of study in the Oxford Student System (OSS). It includes details about programme information such as title, type, length, responsible organisational unit, start date, entry points, attendance type, and more. It also provides guidance on timelines for setting up undergraduate programmes to ensure they are available for the upcoming admissions cycle.
Application of Numerical and Experimental Simulations for the Vibrating Syste...IJERD Editor
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In this work,there may be some requirements of finding out the coupling loss factors of system component.It becomes difficult to exactly know the coupling loss factor by looking at the behavior of the system. For this purpose, the numerical solution developed in this work. Initially, one need to extract the displacement, velocity and energy profiles of the system which has got the components installed for which the coupling loss factor need to be determined. Then the numerical simulations can be run for different coupling loss factor of the vibrating system and the coupling loss factor can be found when the simulation results match with the experimental measurements. In this paper the experimentation is carried out i for the model a)Pre-design application of the work developed. b) Post design application of the work developed. The numerical results converge very well towards the experimental results as the coupling loss factor in simulation is varied towards the actual value. Similarly, for the second approach the experimental results converge towards the simulation results of 0.15 as the coupling loss factor of the damper that is installed on the system is varied towards 0.15.
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The board of education held their regular monthly meeting. They discussed facility improvements at the elementary, middle, and high schools. Issues like leaky roofs, outdated fire alarms, and asbestos removal were noted. The board approved meeting minutes, personnel changes, and vendor payments. They also discussed starting a youth bowling league, home school policies, and bond measures for facility upgrades. The next meeting will cover the bond election, broadband contract, and home school report.
This course covers statistical methods for data mining and machine learning, including linear regression and classification, nonparametric methods, generalized additive models, and tree-based methods. Students will complete homework assignments, a midterm exam, and a final project analyzing a dataset of their choice. The project involves formulating a research question, applying course techniques to answer it, and presenting results in a written report. Evaluation is based on homework, exams, and the final project. The course uses the R programming environment.
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This program provides a library of over 700,000 electrical symbols and components, includes real-time error checking, and enables electrical and mechanical teams to collaborate on digital prototypes built with Autodesk Inventor software. Electrical offers control engineers a competitive edge by helping them save hours of effort so they can spend more time innovating.
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See every online challenge, hackathon, and software project on Devpost. Sort by platform, API, and more!
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