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Mining
Mining for coal is something that is widely known, but did you know that diamonds, uranium, copper, and other non-renewable natural resources are also mined? There are two types of mining; surface and subsurface. Questions about the methods and types of mining, what ores are mined, and related questions should be asked in this category.
4,677 Questions
Mining has been practiced for thousands of years by various ancient civilizations. There isn't a single inventor of mining, but rather it has evolved over time as humans discovered and developed techniques to extract valuable minerals and ores from the earth. The history of mining dates back to prehistoric times and has been crucial for the development of civilizations.
What are metal bearing rocks sought after in mining called?
Metal-bearing rocks sought after in mining are typically called ore deposits. These deposits contain high enough concentrations of valuable metals such as copper, gold, silver, or iron to be economically viable for extraction. Mining companies prospect for ore deposits to extract and process the valuable metals.
(n.)an excavation or pit, usu. open to the air, from which building stone, slate, or the like, is obtained by cutting, blasting, etc.
Category: Building Trades, Mining
an abundant source or supply.
(v.t.)to obtain from or as if from a quarry.
Category: Building Trades, Mining
to make a quarry in.
Category: Mining
What are the minerals and metals mined in the Arctic Lowlands?
Minerals and metals mined in the Arctic Lowlands include gold, silver, lead, zinc, and copper. These resources are extracted through mining operations in areas like Alaska, Canada, and Russia, with companies focusing on responsible and sustainable practices to minimize environmental impact in this delicate ecosystem.
name four agcutural animals and plants in the state arizona
Halite, or rock salt, has been known and used by humans for centuries. It was likely discovered in prehistoric times when humans began to use salt for preserving food and enhancing flavor. Archaeological evidence shows that salt mining dates back to around 6000 BC.
Why were the mining towns in the Kootenays abandoned?
Mining towns would spring up, almost overnight, whenever prospectors discovered ore in sufficient quantity to make mining profitable. Whenever the ore was exhausted, or whenever it was no longer profitable to mine it, the mines would close, miners would be thrown out of work, and people would move elsewhere in search of other jobs.
What did the trappers putters and hewers do in coal mines?
Trappers were responsible for opening and closing ventilation doors to direct airflow, while putters moved coal carts to the surface using ponies or mechanical haulage. Hewers were miners who manually extracted coal from the seam using picks and shovels.
Has mining of copper changed over time?
Yes, mining of copper has evolved over time with advancements in technology and mining techniques. Modern mining methods are more sophisticated and efficient, leading to increased productivity and reduced environmental impact compared to historical mining practices. Additionally, there is now a greater emphasis on sustainability and responsible mining practices in the copper mining industry.
There are several different types of mines. Some are cut on the surface and are dug hundreds of feet downward. Other mines are shafts and drifts that sometime go for many miles through solid rock.
Coal mining began in Pre-History of the Human Race. Some of the oldest cave paintings in the world are drawn by coal. Hunter Gatherers would often identify coal at the land surface level, following by digging deeper if necessary. Populations rapidly found use for coal. Portable Fire, creation of tools, hunting instruments, and food preparation tools among thousands of other uses evolving uses as the Human Race evolved. It is common for Indigenous Americans to use coal as Animal and Human Body / War Paint. Ancient Egyptian Calligraphy scribed some 2,000 years BC has provided written records of large scale coal mining. Coal ash was used to settle the stones of the Great Pyramids, for example.
Miners historically wore protective gear such as helmets with lamps, overalls, boots, and safety belts. These outfits were designed to ensure their safety in the hazardous underground mining environment. Today, miners also use modern equipment such as respirators and high-visibility clothing for added protection.
Iron was discovered and used by ancient civilizations in the Middle East around 3000 BC. It was likely discovered as a byproduct of copper smelting, and its wide availability and usefulness allowed it to revolutionize technology and society.
When and where was iron discovered and by whom?
The middle east, Ancient India and Ancient Greece in about the 12th century BC. It was the 8th BC in central Europe and 6 th BC in Northern Europe. Since it was so long ago no one can say who actually found the first iron ore. new study led by Children's Hospital Oakland Research Institute senior scientist, Elizabeth Theil, Ph.D., is the first to suggest that a small protein or heptapeptide (seven amino acids wrapped into one unit) could be used to accelerate the removal of iron from ferritin. The results of this study may help scientists develop new medications that dramatically improve the removal of excess iron in patients diagnosed with blood diseases such as B-Thalassemia (Cooley's anemia) or Sickle Cell Disease.
The study appears in this month's issue of the Journal of Biological Chemistry and was conducted by Dr. Theil and her co-authors Xiaofeng S. Liu, postdoctoral fellow at Children's Hospital Oakland Research Institute, Marvin J. Miller, Ph.D. and Leslie D. Patterson, a predoctoral student, both from the University of Notre Dame. The scientists knew that the ferritin protein cage had pores that could open and close. It was also known that chelators (a method to detoxify blood) removed iron faster when the pores were open.
"We wanted to prove a hypothesis that a small protein or peptide could bind to ferritin and could be used to regulate ferritin pores," said Dr. Theil. "Our hypothesis was correct. We proved that when a binding peptide of seven amino acids, a heptapeptide, is coupled with Desferal the rate of removal of iron from ferritin is eight times faster." Desferal is currently used to detoxify the blood of patients with iron overload and is a common therapeutic remedy.
Ferritin is a protein that concentrates iron in its inner core or 'cage'. It plays a critical role in understanding iron overload, which can lead to a variety of symptoms including chronic fatigue, weakness, joint pain and arthritis. If left untreated, iron overload can lead to serious problems, including diabetes, liver and heart disease.
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Article adapted by Medical News Today from original press release.
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The study's results are based on laboratory tests. The National Institutes of Health (NIH), the Cooley's Anemia Foundation and Children's Hospital & Research Center Oakland provided funding for this research.
Click here for more information on Dr. Theil's research.
Research at Children's Hospital & Research Center Oakland, CA
Research efforts at Children's Hospital & Research Center Oakland are coordinated through Children's Hospital Oakland Research Institute (CHORI). Children's Hospital Oakland is Northern California's only freestanding and independent children's hospital. CHORI's internationally renowned biomedical research facility brings together seven centers of excellence that are devoted to clinical and basic science research to treat and prevent disease. CHORI has approximately 300 staff members and an annual budget of more than $49 million. The National Institutes of Health is CHORI's primary funding source. The institute is a leader in translational research, bringing bench discoveries to bedside applications. These include providing cures for blood diseases, developing new vaccines for infectious diseases and discovering new treatment protocols for previously fatal or debilitating conditions such as cancers, sickle cell disease and thalassemia, diabetes, asthma, HIV/AIDS, pediatric obesity, nutritional deficiencies, birth defects, hemophilia and cystic fibrosis.
Explian about contact between granite and sandstone?
This is a nonconformity. the contact is between an igneous and sedimentary rock formation. Granite should be on the bottom and sandstone on top, If this is not the case then some type of over-turning has taken place. The depositional envornment allowed sand to acculate over the top of the granite and later consolodated into a rock. This is called a contact. Many years ( perhaps millions) passed) before the whole mass became a rock formation. Can you identify other contacts in the area? If so, you may be able to date this contact and correlate it to other areas in your vicinity. See if you can trace it and look on geologic maps to see if it is mapped. If not, you may have found something that no one has yet discovered.
What equipment was used for mining in the past?
In the past, mining equipment included tools such as pickaxes, shovels, and hand drills for digging and breaking up rocks. As technology advanced, manual tools were supplemented or replaced with machinery like steam engines, drills powered by compressed air, and conveyor belts for transporting materials.
What was the date the first diamonds were mined?
It's possible that diamonds were discovered in India as long ago as 6,000 years.
Without documentation, it might be difficult to settle on a precise year in which diamonds were first mined.
Initially, it's understandable that diamonds were found far from their point of origin, having been washed into riverbeds by water running over the diamond pipe.
Which is correct His shirt is different to mine or His shirt is different from mine?
"His shirt is different from mine" is the correct phrase to use.
What does the mining term piker mean?
A piker in mining means someone who take only small, cautious steps. It can mean anyone who is stingy or a cheapskate or very cautious in gambling. Thought to have been derived from "poor migrants who walked the pike".
What does the term data mining refer to?
Data mining is a new interdiceplenary field that involves large data sets and applying statistics, artificial intelligence, and applying them to help with database management. It is believed that it gives businesses an information advantage on business intelligence.
"Data mining" is a type of computer science, in which one mines data and analyzing it in order to determine patterns and structure for future use. Applications of these structures then can be used in designing databases, artificial intelligence, and statistical analysis.
What is data reduction in terms of data mining?
Data reduction in data mining refers to the process of reducing the volume of data under consideration. This can involve techniques such as feature selection, dimensionality reduction, or sampling to simplify the dataset and make it more manageable for analysis. By reducing the data, analysts can focus on the most relevant information and improve the efficiency of their data mining process.
How Data Mining is useful in MIS?
Data mining in Management Information Systems (MIS) helps organizations to identify trends and patterns within their data that can be used to make better strategic decisions. By analyzing large datasets, data mining can uncover insights that may not be immediately apparent, helping businesses to optimize their operations, improve forecasting accuracy, and enhance decision-making processes.
Seminar topics related with data mining?
Data Mining Seminar report
Introduction
Data mining is the process of extracting patterns from data. Data mining is becoming an increasingly important tool to transform this data into information. It is commonly used in a wide range of profiling practices, such as marketing, surveillance, fraud detection and scientific discovery.
Data mining can be used to uncover patterns in data but is often carried out only on samples of data. The mining process will be ineffective if the samples are not a good representation of the larger body of data. Data mining cannot discover patterns that may be present in the larger body of data if those patterns are not present in the sample being "mined". Inability to find patterns may become a cause for some disputes between customers and service providers. Therefore data mining is not foolproof but may be useful if sufficiently representative data samples are collected. The discovery of a particular pattern in a particular set of data does not necessarily mean that a pattern is found elsewhere in the larger data from which that sample was drawn. An important part of the process is the verification and validation of patterns on other samples of data.
The related terms data dredging, data fishing and data snooping refer to the use of data mining techniques to sample sizes that are (or may be) too small for statistical inferences to be made about the validity of any patterns discovered (see also data-snooping bias). Data dredging may, however, be used to develop new hypotheses, which must then be validated with sufficiently large sample sets.
Read complete article from wikipedia
DataMining Overview
Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
Continuous Innovation
Although data mining is a relatively new term, the technology is not. Companies have used powerful computers to sift through volumes of supermarket scanner data and analyze market research reports for years. However, continuous innovations in computer processing power, disk storage, and statistical software are dramatically increasing the accuracy of analysis while driving down the cost.
Example
For example, one Midwest grocery chain used the data mining capacity of Oracle software to analyze local buying patterns. They discovered that when men bought diapers on Thursdays and Saturdays, they also tended to buy beer. Further analysis showed that these shoppers typically did their weekly grocery shopping on Saturdays. On Thursdays, however, they only bought a few items. The retailer concluded that they purchased the beer to have it available for the upcoming weekend. The grocery chain could use this newly discovered information in various ways to increase revenue. For example, they could move the beer display closer to the diaper display. And, they could make sure beer and diapers were sold at full price on Thursdays.
Data, Information, and Knowledge
Data
Data are any facts, numbers, or text that can be processed by a computer. Today, organizations are accumulating vast and growing amounts of data in different formats and different databases. This includes:
operational or transactional data such as, sales, cost, inventory, payroll, and accounting
nonoperational data, such as industry sales, forecast data, and macro economic data
meta data - data about the data itself, such as logical database design or data dictionary definitions
Information
The patterns, associations, or relationships among all this data can provide information. For example, analysis of retail point of sale transaction data can yield information on which products are selling and when.
Knowledge
Information can be converted into knowledge about historical patterns and future trends. For example, summary information on retail supermarket sales can be analyzed in light of promotional efforts to provide knowledge of consumer buying behavior. Thus, a manufacturer or retailer could determine which items are most susceptible to promotional efforts.
Data Warehouses
Dramatic advances in data capture, processing power, data transmission, and storage capabilities are enabling organizations to integrate their various databases into data warehouses. Data warehousing is defined as a process of centralized data management and retrieval. Data warehousing, like data mining, is a relatively new term although the concept itself has been around for years. Data warehousing represents an ideal vision of maintaining a central repository of all organizational data. Centralization of data is needed to maximize user access and analysis. Dramatic technological advances are making this vision a reality for many companies. And, equally dramatic advances in data analysis software are allowing users to access this data freely. The data analysis software is what supports data mining.
What can data mining do?
Data mining is primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among "internal" factors such as price, product positioning, or staff skills, and "external" factors such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Finally, it enables them to "drill down" into summary information to view detail transactional data.
With data mining, a retailer could use point-of-sale records of customer purchases to send targeted promotions based on an individual's purchase history. By mining demographic data from comment or warranty cards, the retailer could develop products and promotions to appeal to specific customer segments.
For example, Blockbuster Entertainment mines its video rental history database to recommend rentals to individual customers. American Express can suggest products to its cardholders based on analysis of their monthly expenditures.
WalMart is pioneering massive data mining to transform its supplier relationships. WalMart captures point-of-sale transactions from over 2,900 stores in 6 countries and continuously transmits this data to its massive 7.5 terabyte Teradata data warehouse. WalMart allows more than 3,500 suppliers, to access data on their products and perform data analyses. These suppliers use this data to identify customer buying patterns at the store display level. They use this information to manage local store inventory and identify new merchandising opportunities. In 1995, WalMart computers processed over 1 million complex data queries.
The National Basketball Association (NBA) is exploring a data mining application that can be used in conjunction with image recordings of basketball games. The Advanced Scout software analyzes the movements of players to help coaches orchestrate plays and strategies. For example, an analysis of the play-by-play sheet of the game played between the New York Knicks and the Cleveland Cavaliers on January 6, 1995 reveals that when Mark Price played the Guard position, John Williams attempted four jump shots and made each one! Advanced Scout not only finds this pattern, but explains that it is interesting because it differs considerably from the average shooting percentage of 49.30% for the Cavaliers during that game.
By using the NBA universal clock, a coach can automatically bring up the video clips showing each of the jump shots attempted by Williams with Price on the floor, without needing to comb through hours of video footage. Those clips show a very successful pick-and-roll play in which Price draws the Knick's defense and then finds Williams for an open jump shot.
How does data mining work?
While large-scale information technology has been evolving separate transaction and analytical systems, data mining provides the link between the two. Data mining software analyzes relationships and patterns in stored transaction data based on open-ended user queries. Several types of analytical software are available: statistical, machine learning, and neural networks. Generally, any of four types of relationships are sought:
Classes: Stored data is used to locate data in predetermined groups. For example, a restaurant chain could mine customer purchase data to determine when customers visit and what they typically order. This information could be used to increase traffic by having daily specials.
Clusters: Data items are grouped according to logical relationships or consumer preferences. For example, data can be mined to identify market segments or consumer affinities.
Associations: Data can be mined to identify associations. The beer-diaper example is an example of associative mining.
Sequential patterns: Data is mined to anticipate behavior patterns and trends. For example, an outdoor equipment retailer could predict the likelihood of a backpack being purchased based on a consumer's purchase of sleeping bags and hiking shoes.
Data mining consists of five major elements:
Extract, transform, and load transaction data onto the data warehouse system.
Store and manage the data in a multidimensional database system.
Provide data access to business analysts and information technology professionals.
Analyze the data by application software.
Present the data in a useful format, such as a graph or table.
Different levels of analysis are available:
Artificial neural networks: Non-linear predictive models that learn through training and resemble biological neural networks in structure.
Genetic algorithms: Optimization techniques that use processes such as genetic combination, mutation, and natural selection in a design based on the concepts of natural evolution.
Decision trees: Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset. Specific decision tree methods include Classification and Regression Trees (CART) and Chi Square Automatic Interaction Detection (CHAID) . CART and CHAID are decision tree techniques used for classification of a dataset. They provide a set of rules that you can apply to a new (unclassified) dataset to predict which records will have a given outcome. CART segments a dataset by creating 2-way splits while CHAID segments using chi square tests to create multi-way splits. CART typically requires less data preparation than CHAID.
Nearest neighbor method: A technique that classifies each record in a dataset based on a combination of the classes of the k record(s) most similar to it in a historical dataset (where k 1). Sometimes called the k-nearest neighbor technique.
Rule induction: The extraction of useful if-then rules from data based on statistical significance.
Data visualization: The visual interpretation of complex relationships in multidimensional data. Graphics tools are used to illustrate data relationships.
What technological infrastructure is required?
Today, data mining applications are available on all size systems for mainframe, client/server, and PC platforms. System prices range from several thousand dollars for the smallest applications up to $1 million a terabyte for the largest. Enterprise-wide applications generally range in size from 10 gigabytes to over 11 terabytes. NCR has the capacity to deliver applications exceeding 100 terabytes. There are two critical technological drivers:
Size of the database: the more data being processed and maintained, the more powerful the system required.
Query complexity: the more complex the queries and the greater the number of queries being processed, the more powerful the system required.
Relational database storage and management technology is adequate for many data mining applications less than 50 gigabytes. However, this infrastructure needs to be significantly enhanced to support larger applications. Some vendors have added extensive indexing capabilities to improve query performance. Others use new hardware architectures such as Massively Parallel Processors (MPP) to achieve order-of-magnitude improvements in query time. For example, MPP systems from NCR link hundreds of high-speed Pentium processors to achieve performance levels exceeding those of the largest supercomputers.
This report is based on the report http://www.anderson.ucla.edu/
References:
1) http://wwwmaths.anu.edu.au/~steve/pdcn.pdf [PDF]
2) http://www.autonlab.org/tutorials/
3) http://technet.microsoft.com/en-us/library/ms167167.aspx
4)http://www4.stat.ncsu.edu/~dickey/Analytics/Datamine/Powerpoints/Data%20Mining%20Tutorial.ppt[PPT]
5) http://www.dsic.upv.es/~jorallo/dm/index.html
6) http://en.wikipedia.org/wiki/Data_mining
7) http://datamining.typepad.com/
The Foundations of Data Mining
Data mining techniques are the result of a long process of research and product development. This evolution began when business data was first stored on computers, continued with improvements in data access, and more recently, generated technologies that allow users to navigate through their data in real time. Data mining takes this evolutionary process beyond retrospective data access and navigation to prospective and proactive information delivery. Data mining is ready for application in the business community because it is supported by three technologies that are now sufficiently mature:
* Massive data collection
* Powerful multiprocessor computers
* Data mining algorithms
Commercial databases are growing at unprecedented rates. A recent META Group survey of data warehouse projects found that 19% of respondents are beyond the 50 gigabyte level, while 59% expect to be there by second quarter of 1996.1 In some industries, such as retail, these numbers can be much larger. The accompanying need for improved computational engines can now be met in a cost-effective manner with parallel multiprocessor computer technology. Data mining algorithms embody techniques that have existed for at least 10 years, but have only recently been implemented as mature, reliable, understandable tools that consistently outperform older statistical methods.
In the evolution from business data to business information, each new step has built upon the previous one. For example, dynamic data access is critical for drill-through in data navigation applications, and the ability to store large databases is critical to data mining. From the user's point of view, the four steps listed in Table 1 were revolutionary because they allowed new business questions to be answered accurately and quickly.
An excellent article on The Foundations of Data Mining :http://www.thearling.com/text/dmwhite/dmwhite.htm
And a detailed index on Data mining: http://www.thearling.com/index.htm
What is difference between web mining and data mining?
Data mining involves using techniques to find underlying structure and relationships in large amounts of data. The term data mining originated from database marketing industry. Data mining products tend to fall into five categories: neural networks, knowledge discovery, data visualization, fuzzy query analysis and case-based reasoning. Some uses of data mining include cross-selling (analyzing patterns of products frequently purchased together), response modeling (predicting which customers are likely to purchase based on purchase history), and segmentation and profiling (understanding customer segments by profiling archetypal customers). Common applications include mass mailing/telemarketing, medical diagnosis, credit card fraud and computer intrusion detection.
Web mining involves the analysis of Web server logs of a Web site. The Web server logs contain the entire collection of requests made by a potential or current customer through their browser and responses by the Web server. The information in the logs varies depending on the log file format and option selected on the Web server. Analysis of the Web logs can be insightful for managing the corporate e- business on a short-term basis; the real value of this knowledge is obtained through integration of this resource with other customer touchpoint information. Common applications include Web site usability, path to purchase, dynamic content marketing, user profiling through behavior analysis and product affinities.
