Women´s Health Breast Cancer Essay - 1127 Words

Breast Cancer and Women’s Health Breast Cancer is defined as â€Å"a group of solid tumor malignancies arising in the tissues of the breast† (Sarah Crawford, Richard Alder, 2013) in human and other mammals. It can happen to both men and women. For women, breast cancer is one of the leading causes of cancer death. According to National Cancer Institute, in the United States, the 2014 estimated new cases and deaths of female from breast cancer are 232,670 and 40,000, respectively. For male, it’s 430 deaths out of 2,360 new cases. From these numbers, we can see that women in the U.S. are greatly affected by breast cancer, thus, it’s not difficult to imagine the impact on a worldwide level. Although these numbers look frightening, people can†¦show more content†¦For example, if a women’s mother or sister has breast cancer, she may be at higher risk of getting the disease. Mutations in BRCA1 and BRCA2 are leading factors of breast cancers in women; it will actually promote the develop ment of cancer cells. There are other genetic reasons that may increase the risk of breast cancer. For example, according to a research done by some Chinese scholars, â€Å"genetic variants in the vicinity of pre-miR-101-2 were associated with breast cancer risk in the Chinese population.† However, most breast cancer patients do not have family history of this disease. For them, all of the following factors may differently impact the risk of exposure to breast cancer: weight, menstruation age, menopause age, first childbirth age and etc. According to our textbook, early menstruation, later menopause, overweight and having child after age of 30 will increase the risk of getting breast cancer. Other risk factors like medical conditions may also impact the risk of breast cancer. Symptoms and Stages It’s important to learn the symptoms of breast cancer so that we can treat it earlier. Typically, people may found a lump that feels different from other breast tissues. Other noticeable breasts symptoms include shape, size, skin color, texture, itching, pain, swelling, increased sensitivity and etc. The stages of breast cancer depend on tumor size and when it spreads to the other parts of body. In stage 0, also called the situ stage, the tumor is not largeShow MoreRelatedHow do cultural differences affect breast cancer prevalence, prevention, and treatment in African-American, Hispanic/Latina, and Caucasian women livin883 Words   |  4 Pagesdo cultural differences affect breast cancer prevalence, prevention, and treatment in African-American, Hispanic/Latina, and Caucasian women living in the United States? Over the past decade breast cancer has become one of the most predominant diseases in the United States. 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Therefore, breast cancer is caused by uncontrolled growth of cancerous cells in the breasts. Breast cancer has been noted in history for thousands of years. The ancient Greeks first discovered the disease about 3,500 years ago (Mandal, 2013)

Resting Metabolic Rate And Progressive Submaximal Exercise...

EXERCISE PHYSIOLOGY LAB APK4110L - Section # 008 Carissa Insinga 9/22/16 LAB REPORT #2 Resting Metabolic Rate Progressive Submaximal Exercise Test Mike Haischer Introduction In these experiments we talked about the Resting Metabolic Rate and Progressive Submaximal Exercise Testing to determine a subjects’ carbon dioxide production to oxygen consumption. This was done by having examples provided for us to calculate the RMR by using a formula, (X ml/kg/min x weight in kg) / 1000 mL= # L/min, to determine the number of calories burned at rest, as well having subjects perform the standardized treadmill test for 12-15 minutes for the Progressive Submaximal Exercise Testing. Resting Metabolic Rate is the energy expenditure at rest in well rested, fasted state in a supine position. For an accurate reading of RMR, one should not eat or drink within 6 hours before testing, refrain from any physical activity 12 hours prior, and no caffeine/tobacco/medication consumption beforehand. The purpose of finding RMR is to find the number of kilocalories that are required each day. RMR can be depend on factors such as body composition, genetics, gender, and age. A Progressive Submaximal Exercise Test is a test that estimates VO2 max, oxygen consumption, and is performed by gradually increasing the intensity of exercise. This test helps predict the maximal aerobic capacity of an individual as well as measuring the heart rate and VO2 max. WhileShow MoreRelatedSports Performance Evaluation Ioan Stoian National Institute Of Sports Medicine1378 Words   |  6 PagesExercise is termed as an activity requiring physical effort to improve health and fitness. Scientists can use fitness as a measure to compare one person to another (Haskel). Fitness tests can be conducted in a field or lab based setting (Point-of-care athlete testing, a new approach of sport performance evaluation Ioan Stoian National Institute of Sports Medicine, Bucharest, Romania). Fitness as a standard must be quantified to be able to compare individuals (Safrit). Predicting VO2Max can be doneRead MoreEffects of Vigorous and Moderate Exercise on Health-Related Outcomes10786 Words   |  43 Pagesï » ¿Effects of vigorous and moderate exercise on health-related outcomes Introduction In many developed countries, physical inactivity is becoming a public health problem as a result of fewer numbers of people embracing physical activity(Wardle and Steptoe, 2003). Population-based studies that have been conducted in USA and other developed countries in Europe suggest that the education level of individuals directly affects their physical activity (Trost et al., 2002). Thus those with lower levels

Neuron and Chemical Synapse Free Essays

Nervous System II: Anatomy Review 1. The somatic nervous system stimulates ____________ muscle. The autonomic nervous system stimulates ___________ muscle, ____________ muscle, and ___________. We will write a custom essay sample on Neuron and Chemical Synapse or any similar topic only for you Order Now 2. The autonomic nervous system (ANS) consists of two divisions, each innervating the effector organs. The sympathetic nervous system (SNS) generally speeds up everything except digestion. The parasympathetic nervous system (PNS) generally slows down everything but digestion. Signals from the SNS cause the heart rate to _________, while signals from the PNS cause the heart rate to ___________. Signals from the SNS cause smooth muscles of the intestine to _________ contractions, while signals from the PNS cause these muscles to _________ contractions. Signals from the SNS also cause the adrenal gland to _________ epinephrine and norepinephrine. 3. Neurons can excite or inhibit another neuron. Exciting another neuron will increase the chances of a/an ___________________ in the second neuron. Inhibiting another neuron will make the chances of a/an __________________ less likely. 4. Axons from one neuron can synapse with the dendrites or soma of another axon. These synapses are called ______________________ (on dendrites) and _________________________ (on soma). They carry input signals to the other neuron. Axons from one neuron can synapse with the axon terminal of another neuron. These synapses are called ________________________, and they regulate the amount of ________________________ released by the other neuron. 5. The electrical synapse: Electrical current flows from one neuron to another through _________________. These synapses are always (excitatory or inhibitory). Advantages of the electrical synapses: . _______ signal conduction 2. _____________ activity for a group of neurons. 6. The chemical synapse: Chemical synapses are not as fast as electrical but are the most common type of synapse. A chemical, called a/an ______________________, is released from the sending neuron and travels across the ___________________(a gap between the neurons) to the receiving neuron. Advantages of the chemical synapse: 1. The signal can be eithe r ____________ or ____________. 2. The signal can be ______________ as it passes from one neuron to the next. 7. The neuron conducting the impulse toward the synapse is called the __________________ neuron. The axon terminal contains ___________ ____________ filled with ______________________. An action potential in the axon terminal of the _____________ neuron causes the chemical transmitter ____________________ to be released. It diffuses across the synaptic cleft and binds to receptors on the ________________ membrane. These receptors open __________________. The movement of the charged particles causes an electrical signal called a/an _______________________. How to cite Neuron and Chemical Synapse, Essay examples

Business Intelligence Using Big Data Business Operations

Question: Describe about the Business Intelligence Using Big Data for Business Operations. Answer: Introduction Big data is developing continuously as a result it helps in producing a large sum of income from the business operations. It is analyzed that the use case of big data requires some special operations and therefore the structure that is produced with the arrangement of hardware and software provides a technological effect. It is stated by Akerkar (2013), that big data analytics are very much useful for outlining new strategies, which helps in managing technology at a faster rate. It also helps in providing precise results from the skills used. In this report, analysis of big data has been used for forming strategies that would help in supporting the decision-making system of a selected organization. IBM is chosen for implementing big data procedure. The procedures or strategies of big data have been created for IBM. The report also discusses stack technology that is used for implementing data analytics. The procedure is associated with the recovery, storage and creation analysis of data. There are number of features that are provided by the big data, and those features include diversity, rapidity and volume in the examination of big data. The main objective of this report is to implement the big data framework for the usefulness of different business operations in IBM. Identification, creation and discussion of business strategy for using of big data in IBM Identification of business strategy framework The implementation procedure of big data in any business needs a framework for understanding the basic operations (Assuncao et al. 2013). IBM needs to construct and implement a big data structure in the operational structure. The two dimensions on which big data framework is dependent include Business objectives and Data type. Figure1: Strategy Framework of IBM for Big Data Source (Assuno et al. 2015) Creation of Business Strategy Framework Transactional Data Methods used Business Intelligence: The technique of business intelligence that is used by IBM is user friendly and thus it helps in interactive and multidimensional data analysis (Begoli and Horey 2012). It also provides different features such as rolling up, reporting the capabilities tools and many more. Cluster analysis: It helps in analyzing those objects that have similar attributes and properties. Data Mining: It is used by the organization for extracting as well as processing new patterns. Predictive Models: IBM creates models in order to predict the results from an activity (Buhl et al.2013). SQL: SQL is used for extracting, inserting and managing the values or data in a database. Vendors It helps in reporting the services and the analysis from the server with the help of Microsoft SQL (Chaudhuri 2012). It also helps in providing the business objectives from SAS, SAP, and Business intelligence by using Oracle. Non- Transactional Data Methods Used Crowd Sourcing: IBM uses the technique of crowd sourcing for getting the required services, content or ideas by soliciting contributions from a huge mass of people (Chen et al. 2012). Textual Analyzing: The organization uses the method of textual analysis for analyzing the different content of communication rather using the structure of the content. Analysis of Sentiments: The organization uses the process of sentiment analysis for determining the results of analysis. The results can be positive, negative or neutral (Demirkan and Delen 2013). Network Analysis: IBM uses the procedure of network analysis for calculating the relationship between the elements of networks and nodes. Vendors Visible technologies, Watson services, Radian6 and many more; Discussion of Business strategy framework of IBM The different business strategy framework of IBM includes: Performance Management: It is very much easy as well as helpful in accepting the analytics as well as database of big data. Performance management is useful in order to determine the multidimensional queries and related analytics in the organization (Gandomi and Haider 2015). For example, the big data strategy framework is used for analyzing the purchasing activity, expected turnover of the organization. It helps the managers in making short times and long time decision as well as plans. The functionality of different business intelligence tools is very much helpful for improving both the management and the business operations of the organization. Data Exploration: The data exploration framework is helpful in using the different procedures of data analytics in order to experiment and answer the questions, which has not been properly thought by the management of IBM (Jagadish et al. 2014). It also helps in implying the different predictive models for managing the user-based behavior in different sections of operation such as management and in transaction department of IBM. Big data helps the organization by supplying information and by designing strategies that would help in retaining the various segments of the users. Social Analytics: Social analytics framework is very much helpful for the organization as it helps in measuring the huge amount of non-transactional data such as reviews and platform of social media. The big data strategy is categorized by the social analytics (Katal et al. 2013). The three wide divisions of big data strategy include awareness, engagement and reach of the analysis. Engagement is helpful in measuring the level of interaction and involvement among the team members. Awareness helps in checking the exposure of knowledge in very group members. The members of the organization are quantified based on the level of knowledge and about any particular business function. Decision Science: Decision science helps in analyzing the data that are not related in to the transaction. The big data helps in exploring the rules and regulation in order to focus on the hypothesis and field research (Lazer et al. 2014). It is very much helpful for the IBM for conducting different feedbacks from the community. It helps in fitting both the ideas and it is also used for developing the value of a product. In order to perform the text analysis of sentiment, it needs listening tools (Liebowitz 2013). IBM uses the tools in order to measure the topics that are related with development and interested products. Identification and aligning the business strategys initiative, objective and the task of IBM Identification of Business strategy Aligning the formed strategy with objective, Initiatives and task Integration of multiple strategies of big data Big data can be implemented for multiple uses and thus the company can levitate for combining the strategies of big data (Lohr 2012). For example, Performance management is useful in gaining better production for forming synchronization with the demands and needs of the customers. Building capabilities of big data It is a technology or a process that is required for supporting the initiatives of big data. A plan must be devised by the expertise in order to implement the strategy of big data (Mayer-Schnberger and Cukier 2013).The organization, IBM has to hire skilled managers for guiding the employees who take care of the big data. It is helpful for creating specific group structures in order to focus on the big data analytics and business management. Proactive creation of big data policy IBM needs to update itself with the guidelines and policies for using the big data (Minelli et al. 2012). It is helpful in accessing non-transactional and social data for creating and accessing business operations. Therefore, IBM is greatly influenced by the security and privacy of the business operations. Analysis of Technology Stack for IBM big data The technology stack of big data analytics of IBM has analyzed some components, which are helpful in forming the analytics. Both external as well as internal data sources are required in the market analysis of IBM, which are shown by the different sources (Moniruzzaman and Hossain 2013). For analyzing data, it creates a lake of data. In order to perform the data analytics procedure, stack technology consists of 3V is which are variety, velocity and volume. Volume consists of various amounts of data that needs to be stored and managed. Variety consists of various types of data that are used in the analytics of big data (Raghupathi and Raghupathi 2014). Variety means the various types of data that are used in the big data analytics. Velocity is defined as a speed in which the data in stack technology are recorded and processed. There are different kinds of stack technologies that are used in order to create the architecture of big data analytics in IBM. PIG: A scripting technology is used for processing and analyzing huge quantity of data sets (Sagiroglu and Sinanc 2013).In order to access the engines, apache pig consists of an architectural structure, which also helps in storing clusters of data. YARN: It is one of the acronyms for resource navigator, which is helpful in large-scale data application for distributing the operating system (Shroff et al. 2013).It is very much helpful in combining both the synchronized as well as central resource managers for reconciliation. Hive: This stack technology is useful in summarizing, querying and for analyzing the data which will be helpful for the business insights (Vera-Baquero et al. 2013).The tables that are present in hive are organized in the pattern of granular units for creating the taxonomy. Data analytics and MDM for supporting the business intelligence and decision making of IBM Data analytics: There are three challenges that are required in the management process of big data. The challenges are sorted with the help of the big data analytics. Right data is selected by them in order to handle the operations of data analytics and for using the insights that are gained for transforming the different operations of business (Waller and Fawcett 2013). Big data analytics helps in managing the big data and helps in advancing their analytics. It is very much beneficial in order to deal with the lack of analytical talent that is needed for implementing the big data analytics. It is helpful in creating new roles for job. Big data is acting as revolution in the fields of analytics measurement and administration. The big data analytics is helpful in driving data for the process of decision-making in the business operations of IBM. There is a lot of difference between the data driven and information collected in IBM. It is analyzed that the chances of data lose is more when the data are stored for longer period (Wixom et al. 2014). Big data analytics and business analytics helps in analyzing the data that were stored long before as a result they helps in creating effective results by using it. IBM is benefitted by the big data analytics because each data has role, which in turn helps in assisting the process of decision-making. Master Data management: A method helps in identifying the most important as well as critical data of IBM in order to create a singular source of data for managing the business. It involves different technological solutions to improve the big data processing as well as management, which includes data integration, quality, and management (Wu et al. 2014).The following characteristic of MDN is helpful in supporting the decision-making system of the organization and its business intelligence. Standard Data view: It is helpful in creating single view in order to authorize the critical business management. The MDN process is used by the IBM data analytics in order to resolve the issues such as data disputation, duplication and many more (Begoli and Horey 2012). For example, two people having the same first name will create a trouble in entering the data as a result big data analytics can be used for drawing their last name and addresses in order to distinguish between the two individuals. Complete overview of the relationship: MDN is a big data analytics that helps in identifying the relationship among the different data entity. It will help the organization in combining one data entity with the other based on the relationship of the coefficient. For example, IBM uses MDN to store the names of the purchaser. Managing interactions: It is used in order to integrate the occurrence and transaction of social interaction between the clients and the operators of the business (Chaudhuri 2012). It will create a bridge between the customers and data channel partners in order to complete the views of the customers of IBM. Design features: The factors behind the efficient and proper management of big data analytics include flexibility of the design model, Variability of model operation and scalability functions (Demirkan and Delen 2013). IBM uses all this features in order to use its data analytics. The MDN system does not need coding for its implications therefore and thus it can be easily applied in IBM. The agility of the software process is helpful in creating the focus of the database on the success of the customers. Analyzing support of NoSQL for big data analytics in IBM NoSQL or non-related SQL is helpful in giving various facilities for the big data analytics, which includes scalability, observable alternative different association of strengths, many multinational organizations like Amazon (Gandomi and Haider 2015). Google uses big data NoSQL for working with the operational database. NoSQL has different characteristics for user-friendly advance, which helps in creating and easing the operations of the business database administration properly (Liebowitz 2013). NoSQL is helpful in empowering most of the organizations. NoSQL consists of various systems such as payroll systems, reluctance system and data processing system. NoSQL will be helpful in processing unpredictable as well as unstructured information system in order to provide help to the big data information management of IBM (Lohr 2012). NoSQL assists in solving different bottleneck errors by processing the unstructured database System (Minelli et al. 2012). Hence, the big data purpose of IBM can be managed by using the system of NoSQL. NoSQL is not required for knowing the structure beforehand. This is because the system does not lack schema orientation (Raghupathi and Raghupathi 2014). The system is helpful in solving the data, which is arised due to acid property of the data analytics. Different types of NoSQL databases and its use in big data of IBM Various types of NoSQL databases Description Use in Big Data use case of IBM Key value store It consists of big hash based table of keys and values Example: Riak used by Amazon The schema format of this NoSQL database is helpful in forming the database that is value based. This type of key is helpful in creating as well as generating auto type of data base system (Sagiroglu and Sinanc 2013). IBM can use the system for creating auto-generated database in big data analytics. Document based store It helps in storing elements that are made up of tagged elements Example: couchDB The database of NoSQL format uses various types of key and value pair in order to store the values of the data (Shroff et al. 2013). It is very much helpful for IBM for creating structure and encoding for managing the big data analytics Column based store Each block of storage consists of data that is formed from one column of the system table Example: Cassandra and HBase In this type of database schema, the data is stored in row cells instead of column cells. It is helpful for IBM as it provides the organization with the ease of accessing and fast searching (Waller and Fawcett 2013).The big data that is stored in this type off scheme is helpful in aggregating the data on a single column. Graph based It is a type of database that uses nodes and edges for storing and representing data over the system table Example: Neo4J Graph based NoSQL database schema is pictorial representation of database that in based on the structure of flexible data values structure (Assuncao et al. 2013). It is helpful as it provides IBM the ease of transformation of scheme from one model structure to different model structure (Begoli and Horey 2012). The graph consists of edges and nodes therefore it in helpful in creating elation among the nodes of the data. Role of social media in the decision making process of the organization The social networking plays a crucial function in big data analytics and management of database. It is very much helpful in creating advertisement of the database administration of big data analytics (Buhl et al. 2013). It is helpful in the process of proficient decision-making processes, which became social. The habitual influential cycle of the functions is disrupted with the help of social media and networking. The manager uses the social networking for informing as well as validating the decisions that are related with the big data. According Demirkan and Delen (2013), the facilities that the social media provides includes: Helps in searching the feedbacks and responses of the customers or clients It helps in enhancing the partnership with others. The reliability of the information is improved (Gandomi and Haider 2015). Business decisions are researched over the global market Helps in accessing information or data that are unavailable everywhere It helps in keeping eye on the co-worker and colleagues (Lazer et al. 2014) Evaluation of Big Data Value creation process The big data formation process is vast probable in any business. The procedure is very much useful in forming a link between the providers and the customers. The procedure of big data consists of various processes, which includes inventory, manufacturing distribution and marketing (Lohr 2012).The products or services have to go through number of procedures in order to meet the needs and necessities of the customers. It is stated by Demirkan and Delen (2013), that the steps that are helpful for the company includes: Manufacture of goods Creating inventory of products and services Study of physical resources (Waller and Fawcett 2013). delivery to retail shops Mass advertising of goods It is stated by Moniruzzaman and Hossain (2013), the value creation procedure of IBM includes: Increase in the number of clients Improving the techniques of the market Optimizing the supply chain (Sagiroglu and Sinanc 2013). Reducing the price of the stir Increasing the turnover of the inventory Enhancing the effectiveness of hiring Conclusion It is concluded from the report that big data analytics is used in order to increase the revenue of an organization. Both hardware as well as software technology have affected the operations of the business. The big data analytics is very much useful in meeting the demands of the customers. It is analyzed that in this assignment IBM is selected for the implementing procedure of big data analytics. The strategies that are used for big data analytics are created using the formation or creation procedure. It is concluded that the big data analytics is very much helpful in creating new technologies and it is extremely helpful in meeting the demands of the customers. References Akerkar, R. ed., 2013.Big data computing. CRC Press. Assuncao, M.D., Calheiros, R.N., Bianchi, S., Netto, M.A. and Buyya, R., 2013. Big Data computing and clouds: challenges, solutions, and future directions.arXiv preprint arXiv:1312.4722. Assuno, M.D., Calheiros, R.N., Bianchi, S., Netto, M.A. and Buyya, R., 2015. Big Data computing and clouds: Trends and future directions.Journal of Parallel and Distributed Computing,79, pp.3-15. Begoli, E. and Horey, J., 2012, August. Design principles for effective knowledge discovery from big data. InSoftware Architecture (WICSA) and European Conference on Software Architecture (ECSA), 2012 joint working IEEE/IFIP conference on(pp. 215-218). IEEE. Buhl, H.U., Rglinger, M., Moser, F. and Heidemann, J., 2013. Big data.Business Information Systems Engineering,5(2), pp.65-69. Chaudhuri, S., 2012, May. What next?: a half-dozen data management research goals for big data and the cloud. InProceedings of the 31st ACM SIGMOD-SIGACT-SIGAI symposium on Principles of Database Systems(pp. 1-4). ACM. Chen, H., Chiang, R.H. and Storey, V.C., 2012. Business Intelligence and Analytics: From Big Data to Big Impact.MIS quarterly,36(4), pp.1165-1188. Demirkan, H. and Delen, D., 2013. Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud.Decision Support Systems,55(1), pp.412-421. Gandomi, A. and Haider, M., 2015. Beyond the hype: Big data concepts, methods, and analytics.International Journal of Information Management,35(2), pp.137-144. Jagadish, H.V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J.M., Ramakrishnan, R. and Shahabi, C., 2014. Big data and its technical challenges.Communications of the ACM,57(7), pp.86-94. Katal, A., Wazid, M. and Goudar, R.H., 2013, August. Big data: issues, challenges, tools and good practices. InContemporary Computing (IC3), 2013 Sixth International Conference on(pp. 404-409). IEEE. Lazer, D., Kennedy, R., King, G. and Vespignani, A., 2014. The parable of Google flu: traps in big data analysis.Science,343(6176), pp.1203-1205. Liebowitz, J. ed., 2013.Big data and business analytics. CRC Press. Lohr, S., 2012. The age of big data.New York Times,11. Mayer-Schnberger, V. and Cukier, K., 2013.Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt. Zaman, N., Seliaman, M.E., Hassan, M.F. and Marquez, F.P.G., 2015.Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence. Information Science Reference. Minelli, M., Chambers, M. and Dhiraj, A., 2012.Big data, big analytics: emerging business intelligence and analytic trends for today's businesses. John Wiley Sons. Moniruzzaman, A.B.M. and Hossain, S.A., 2013. Nosql database: New era of databases for big data analytics-classification, characteristics and comparison.arXiv preprint arXiv:1307.0191. Raghupathi, W. and Raghupathi, V., 2014. Big data analytics in healthcare: promise and potential.Health Information Science and Systems,2(1), p.1. Sagiroglu, S. and Sinanc, D., 2013, May. Big data: A review. InCollaboration Technologies and Systems (CTS), 2013 International Conference on(pp. 42-47). IEEE. Shroff, G., Dey, L. and Agrawal, P., 2013. Social Business Intelligence Using Big Data.CSI Communications, pp.11-16. Vera-Baquero, A., Colomo-Palacios, R. and Molloy, O., 2013. Business process analytics using a big data approach.IT Professional,15(6), pp.29-35. Waller, M.A. and Fawcett, S.E., 2013. Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management.Journal of Business Logistics,34(2), pp.77-84. Wixom, B., Ariyachandra, T., Douglas, D., Goul, M., Gupta, B., Iyer, L., Kulkarni, U., Mooney, J.G., Phillips-Wren, G. and Turetken, O., 2014. The current state of business intelligence in academia: The arrival of big data.Communications of the Association for Information Systems,34(1), p.1. Wu, X., Zhu, X., Wu, G.Q. and Ding, W., 2014. Data mining with big data.IEEE transactions on knowledge and data engineering,26(1), pp.97-107.

A Brief History of Women in Higher Education

A Brief History of Women in Higher Education While more women than men have attended college in the U.S. since the late 1970s, female students were largely prevented from pursuing higher education until the 19th century. Before then, female seminaries were the primary alternative for women who wished to earn a higher degree. But women’s rights activists fought for higher education for female students, and college campuses turned out to be fertile ground for gender equality activism. Female Grads During the 17th and 18th Centuries Before the formal desegregation of mens and womens higher education, a small number of women graduated from universities. Most were from wealthy or well-educated families, and the oldest examples of such women can be found in Europe. Juliana Morell earned a law doctorate in Spain in 1608.Anna Maria van Schurman attended the university at Utrecht, Netherlands, in 1636.Ursula Agricola and Maria Jonae Palmgren were admitted to college in Sweden in 1644.Elena Cornaro Piscopia earned a doctor of philosophy degree at the University of Padua, Italy, in 1678.Laura Bassi earned a doctor of philosophy degree at the University of Bologna, Italy, in 1732, and then became the first woman to teach in an official capacity at any European University.Cristina Roccati received a university degree in Italy in 1751.Aurora Liljenroth graduated from college in Sweden in 1788, the first woman to do so. U.S. Seminaries Educated Women in the 1700s In 1742, the Bethlehem Female Seminary was established in Germantown, Pennsylvania, becoming the first institute of higher education for women in the United States. It was founded by the Countess Benigna von Zinzendorf, daughter of Count Nicholas von Zinzendorf, under his sponsorship. She was only 17 years old at the time.  In 1863, the state officially recognized the institution as a college and the college was then permitted to issue bachelor’s degrees. In 1913, the college was renamed the Moravian Seminary and College for Women, and, later, the institution became co-educational. Thirty years after Bethlehem opened, the Moravian sisters founded Salem College in North Carolina. It since became the Salem Female Academy and is still open today. Womens Higher Ed at the Turn of the 18th Century In 1792, Sarah Pierce founded the Litchfield Female Academy in Connecticut. The Rev. Lyman Beecher (father of Catherine Beecher, Harriet Beecher Stowe, and Isabella Beecher Hooker) was among the lecturers at the school, part of the republican motherhood ideological trend. The school focused on educating women so that they could be responsible for raising an educated citizenry. Eleven years after Litchfield was established, Bradford Academy in Bradford, Massachusetts, began admitting women. Fourteen men and 37 women graduated in the first class of students. In 1837, the school changed its focus to only admit women.   Options for Women During the 1820s In 1821, Clinton Female Seminary opened; it would later merge into the Georgia Female College. Two years later, Catharine Beecher founded the Hartford Female Seminary, but the school did not survive beyond the 19th century. Beechers sister, writer Harriet Beecher Stowe, was a student at Hartford Female Seminary and later a teacher there.  Fanny Fern, a children’s author, and newspaper columnist, also graduated from Hartford. Lindon Wood School for Girls was founded in 1827 and continued as Lindenwood University. This was the first school of higher education for women that was located west of the Mississippi. The next year, Zilpah Grant founded Ipswich Academy, with Mary Lyon as an early principal. The purpose of the school was to prepare young women to be missionaries and teachers. The school took the name Ipswich Female Seminary in 1848 and operated until 1876. In 1834, Mary Lyon established the Wheaton Female Seminary in Norton, Massachusetts. She then started the Mount Holyoke Female Seminary in South Hadley, Massachusetts, in 1837. Mount Holyoke received a collegiate charter in 1888, and today the schools are known as Wheaton College and Mount Holyoke College. Schools for Female Students During the 1830s Columbia Female Academy opened in 1833. It later became a full college and exists today as Stephens College. Now called Wesleyan, Georgia Female College was created in 1836 specifically so women could earn bachelor’s degrees. The following year, St. Mary’s Hall was founded in New Jersey as a female seminary. It is today a pre-K through high school named Doane Academy. More Inclusive Higher Ed From the 1850s Onward In 1849, Elizabeth Blackwell graduated from Geneva Medical College in Geneva, New York. She was the first woman in America admitted to a medical school and the first in the United States to receive a medical degree. The next year, Lucy Sessions made history when she graduated with a literary degree from Oberlin College in Ohio. She became the First African-American female college graduate. Oberlin was founded in 1833 and admitted four women as full students in 1837.  Only a few years later, more than a third (but less than half) of the student body were women. After Sessions earned her history-making degree from Oberlin, Mary Jane Patterson, in 1862, became the first African-American woman to earn a bachelors degree. Higher education opportunities for women really expanded during the late 1800s. The Ivy League colleges had been solely available to male students, but companion colleges for women, known as the Seven Sisters, were founded from 1837 to 1889.

Definition and Examples of Target Domain in Metaphor

Definition and Examples of Target Domain in Metaphor In a conceptual metaphor, the target domain is the  quality or experience described by or identified with  the source domain. Also known as the  image recipient. In Introducing Metaphor (2006), Knowles and Moon note that conceptual metaphors equate two concept areas, as in ARGUMENT IS WAR. The term source domain is used for the concept area from which the metaphor is drawn: here, WAR. Target domain is used for the concept area to which the metaphor is applied: here, ARGUMENT. The terms target and source were introduced by George  Lakoff and Mark Johnson  in Metaphors We Live By (1980).  Although the more traditional terms tenor and vehicle (I.A. Richards, 1936)  are roughly equivalent to target domain and source domain, respectively, the traditional terms fail to emphasize the interaction between the two domains.  Ã‚  As  William P. Brown points out, The  terms target domain and source domain  not only acknowledge a certain parity of import between the metaphor and its referent but they also illustrate more precisely the dynamic that occurs when something is referenced metaphorically- a superimposing or unilateral mapping of one domain on another (Psalms, 2010). See Examples and Observations below. Also see: Cognitive LinguisticsConceptual BlendingConceptual DomainMetaphorical ClusterTenorWhat Is a Metaphor? Examples and Observations The Two Domains The two domains that participate in conceptual metaphor have special names. The conceptual domain from which we draw metaphorical expressions to understand another conceptual domain is called source domain, while the conceptual domain that is understood this way is the target domain. Thus, life, arguments, love, theory,  ideas, social organizations, and others are target domains, while journeys, war, buildings, food, plants, and others are source domains. The target domain is the domain we try to understand through the use of the source domain. - Zoltan Kovecses, Metaphor: A Practical Introduction. Oxford University Press, 2001   ​Target and Source Domains in LOVE IS A JOURNEY Metaphorical concepts fulfill all their functions . . . through a network of metaphorical expressions. . . . [T]ake the following example: Conceptual metaphor:LOVE IS A JOURNEYMetaphorical expressions:this relationship is foundering,we are going nowhere,this relationship is a dead-end street,we are at a crossroads, etc. . . . Metaphors connect two conceptual domains: the target domain and the source domain. In the course of metaphorical processes the source domain corresponds to the target domain; in other words, there is a mapping or a projection between the source domain and the target domain. The target domain X is understood in terms of the source domain Y. For example, in the case of the metaphorical concept mentioned above, LOVE is the target domain whereas JOURNEY is the source domain. Whenever JOURNEY is mapped onto LOVE, the two domains correspond to each other in a way which enables us to interpret LOVE as a JOURNEY. - Andrs Kertà ©sz, Cognitive Semantics and Scientific Knowledge. John Benjamins, 2004Mappings -  The term mapping  comes from the nomenclature of mathematics. Its application in metaphor research basically means that features from a  source domain (e.g. OBJECTS) are mapped onto a target domain (e.g. IDEAS). The term metaphorical expression refers to the surface realization of such a cross-domain mapping which is virtually what the term metaphor used to refer to (Lakoff 1993:203).(Markus Tendahl, A Hybrid Theory of Metaphor. Palgrave Macmillan, 2009)  Ã‚  Ã‚  It is possible for two different parts of a sentence to make use of two distinct metaphorical mappings at once. Consider a phrase like, within the coming weeks. Here, within makes use of the metaphor of time as a stationary landscape which has extension and bounded regions, whereas coming makes use of the metaphor of times as moving objects. This is possible because the two metaphors for time pick out different aspects of the target domain.(George Lakoff, The Contemporary Theory of Metaphor, Metaphor and Thought, ed. by A. Ortony. Cambridge University  Press, 1993)

The Resiliency of the Adjutant General Officer Article

The Resiliency of the Adjutant General Officer - Article Example The department is responsible for the total security of the state; thus, vital to the military. Basing on the ideologies of Shamir, the officer may experience short-term challenges that show stress-related concerns. Therefore, the officer should find the best method to manage the symptoms associated with stressful areas and improve performance. The resiliency of such an officer is evident in divergent pillars strengths such as family, emotional, sacred, bodily and communal. In relation to family, a secure, helpful and caring family is vital to the life of the officer as it assists in dealing with tension. The army itself is a family; thus, the officer has the opportunity to count on friends whenever there is a need. Interestingly, the officer has enormous responsibilities and can overcome this challenge by selecting soldiers who will act as assistants creating more time with their family. Seward asserts that physical resiliency of the officer is paramount as it does not involve the outside body fitness but also how the officer achieves divergent processes under tension; thus, the need for aerobic strength, vigor, and patience. This involves a body in good physical shape built and maintained using quality behaviors and sane choices. In order to have a healthy body, the officer should frequently exercise. In addition, proper eating habits are important. The Adjutant General Officer exercises emotional resiliency; thus, their ability to tackle difficulties with hope. Indeed, the officer may encounter rude and irresponsible soldiers; thus, should always exercise calmness in order to think clearly and make right decisions. In addition, it is fundamental to minimize anxiety as it creates concerns that will spread to others.