Procurement Strategies for Companies when Encountering Natural Disasters

Free Essays on Avian Flu

1) What is a virus? Any of various simple submicroscopic parasites of plants, animals, and bacteria that often cause disease and that consist essentially of a core of RNA or DNA surrounded by a protein coat. Unable to replicate without a host cell, viruses are typically not considered living organisms. 2) Name of virus to be studied- Avian flu virus 3) Background research Avian influenza is caused by type A influenza virus. The symptoms can vary from a mild disease with little or no mortality to a highly fatal, rapidly spreading epidemic (highly pathogenic avian influenza) depending on the infecting virus strain, host factors, and environmental stressors. More avian influenza viruses have been isolated from ducks than any other species although most free-flying birds may also be infected including shorebirds, gulls and other seabirds. Waterfowl are more resistant to avian influenza than are domestic poultry. Viruses that cause no obvious disease in waterfowl can be highly pathogenic (rapidly fatal) in domestic poultry. Among domestic poultry species, turkeys are more commonly infected than are chickens. Waterfowl act as a reservoir of avian influenza virus by carrying the virus in their intestinal tract and shedding it in their feces. Avian influenza viruses are spread to susceptible birds through inhalation of influenza particles in nasal and respiratory secretions and from contact with the feces of infected birds. 4) Symptoms- Signs including coughing, sneezing ruffled feathers, swollen heads, and nervous signs like depression and diarrhea may occur together or singly. In some cases, birds die rapidly without clinical signs of disease. 5) Treatment There are two anti-viral drugs, Amantadine and Rimantadine.... Free Essays on Avian Flu Free Essays on Avian Flu 1) What is a virus? Any of various simple submicroscopic parasites of plants, animals, and bacteria that often cause disease and that consist essentially of a core of RNA or DNA surrounded by a protein coat. Unable to replicate without a host cell, viruses are typically not considered living organisms. 2) Name of virus to be studied- Avian flu virus 3) Background research Avian influenza is caused by type A influenza virus. The symptoms can vary from a mild disease with little or no mortality to a highly fatal, rapidly spreading epidemic (highly pathogenic avian influenza) depending on the infecting virus strain, host factors, and environmental stressors. More avian influenza viruses have been isolated from ducks than any other species although most free-flying birds may also be infected including shorebirds, gulls and other seabirds. Waterfowl are more resistant to avian influenza than are domestic poultry. Viruses that cause no obvious disease in waterfowl can be highly pathogenic (rapidly fatal) in domestic poultry. Among domestic poultry species, turkeys are more commonly infected than are chickens. Waterfowl act as a reservoir of avian influenza virus by carrying the virus in their intestinal tract and shedding it in their feces. Avian influenza viruses are spread to susceptible birds through inhalation of influenza particles in nasal and respiratory secretions and from contact with the feces of infected birds. 4) Symptoms- Signs including coughing, sneezing ruffled feathers, swollen heads, and nervous signs like depression and diarrhea may occur together or singly. In some cases, birds die rapidly without clinical signs of disease. 5) Treatment There are two anti-viral drugs, Amantadine and Rimantadine....

Normal Branding Theory - Promoting Brand Identity Essay

Normal Branding Theory - Promoting Brand Identity - Essay Example The behavioural mannerisms of the audiences, both primary and secondary are significant since these shape up the sale of these brands or as exclusively one can state, the marketing of the same. (Jackson, 2004) Brands have overtaken the retail units by storm. They seem to be everywhere. One brand is ‘in’ today and it might just be replaced or cannibalized by its smaller unit every other week. This means that there is a lot of diversification which is taking place and if seen from the competitive standpoint, this is something that boosts competition and gives rise to a healthy one. (Keller, 2003) But then again, there are drawbacks in such a situation. Since how many brands can remain in the awareness set of the consumer that the brand is actually hitting upon? The answer to this is not only confusing but also perplexing to state the least. (Faust, 1994) Further, Douglas (2004) has asserted the way in which brands have attained the position of icons on their own due right and place. They have outsmarted the manner in which other brands are perceived and this is a significant basis for their long-term success within the relevant markets nonetheless. The normal branding theory thus speaks of the ways through which brands are propagated across a number of different channels and whether these communicate the essence of the product in entirety or otherwise. The proliferation of brands in a tremendous amount is a tantamount to serious market activity and shuffling in of brands at a breakneck speed which is all the more pleasing for the market indicators and the business as a whole. The manner in which this proliferation has come about has brought serious and grave concerns on the minds of the people who are in charge of running the whole show but then again it is in direct proportion with the supply and demand theory which we discuss in the coming lines. (Brache, 2007) Brands offer some sort of value to a particular set of audience and it is up to the brand manager and his team that this audience is narrowed down as far as possible.  Ã‚  

Low Life Expectancy in the Developing World Essay

Low Life Expectancy in the Developing World - Essay Example From this discussion it is clear that  life expectancy of different countries is different. Developed countries normally have better life expectancy because of the better care it provides to the wellbeing of its citizens. On the other hand, developing countries or underdeveloped countries cannot spend much on the health care sector and subsequently the people in these countries may have a low life expectancy compared to that in the developed countries. African countries are famous for low life expectancy not because of poor economy alone, but because of the life styles also.As the paper highlights  the increasing number of severe diseases is the major reason for low life expectancy.   AIDS, Heart attacks, cancer, stroke, high blood pressure, Cholesterol, diabetics are some of the major diseases which lowers the life expectancy of the developing world. Cancer is one of the major problems in the developing world.  Africa is a continent which is infamous for AUDS problems. Afric ans, have poor habits in their sexual life and they do not care much about the consequences while engage in unsafe sexual activities. â€Å"Sub-Saharan Africa is more heavily affected by HIV and AIDS than any other region of the world. An estimated 22.5 million people are living with HIV in the region - around two thirds of the global total†.  The African culture and life styles are major culprits for the increased AIDS problems in this region. Africans are fun loving people and they have the habit of unsafe sex.

Google Case Study Example | Topics and Well Written Essays - 250 words

Google - Case Study Example rnment’s stringent laws, Google agreement for self censorship is the right alternative as it provides it with option to tap the huge market for its other products like android services. Hence, I agree with Schmidt that some information is better than no information. Schmidt is right in his assertion as Google is a business proposition with the basic objective of disseminating information. In the contemporary environment of global competition, changes within business strategy become essential and decision to self censor in China is part of its strategy for global expansion. Google’s decision therefore is right conforms to its wider objective of business goals and mission. While Google’s business strategy is fundamentally based on free access to full information, it has to forego its principle of providing full access to information as per guidelines and restrictions of Chinese government. Hence, the Chinese customers cannot access information that is deemed objectionable by the Chinese government. But in the highly competitive global business, China presents huge scope of business expansion and therefore its decision to self censor is

Big Data as an e-Health Service

Big Data as an e-Health Service Abstract: Bigdata in healthcare relates to electronic health records, patients reported outcomes all other data sets.It is not possible to maintain large and complex data with traditional database tools. After many innovation researches done by researchers Big Data is regenerating the health care, business data and finally society as e-Health .The study on bigdata e-health service. In this paper we come to know why the current technologies like STORM, hadoop, MapReduce can’t be applied directly to electronic-health services. It describes the added capabilities required to make the electronic-health services to become more practical. Next this paper provides report on architecture of bigdata e-health services that provides meaning of e-services, management operations and compliance. Keywords: Introduction to big data, different types of technologies of bigdata, advantages of bigdata, applications of big data, solutions of e-health services, big data as a service provider, e-health data operation management. Introduction: What is bigdata? Bigdata consisting of extremely huge amount of data sets which consists all kinds of data and it is difficult to extract. It can be described by the characteristics like variety, velocity, volume and variability. Variety It consists of data like structured, unstructured and semi structured data Structured data consists of databases, small scale health personal records, insurances, data wares, Enterprise Systems(like CRM, ERP etc) Unstructured data consists of analog data, Audio/video streams. Treatment data, research data Semi Structured data consists of XML, E-Mail, EDI. Velocity Velocity depends on time Sensitivity   It also depends on streaming Volume   It may consists of large quantities of files or small files in quantity   for example , now a days single person can have more than one Gmail account. When he wants to login into a gmail accounts the system generates log files . If a person login into gmail account multiple times through his different accounts then , the system generates huge number of log files that is stored in a servers using bigdata. Variability   It shows the inconsistency of data depends on variation of time period .It may be a problem for analyzing the data. Historically Bigdata in health care industries generate huge amount of electronic health datasets are so complex and difficult to manage by using the traditional software’s or hardware nor by using some database management tools. Now the current trend is to make these huge amount of data as Digitalization so that this whole digital healthcare system will transform the whole healthcare process will become more efficient and highly expensive cost will be reduced. In other words Bigdata in healthcare is evolving into a propitious field for providing perception from large set of data and it produces outcomes which reduces the cost. Bigdata in healthcare industry is stunning not only because of huge volume of datasets like clinical records of patients health reports, patient insurance report, pharmacy, prescriptions , medical imaging , patient data in electronic patient records etc but also multiplicity of data types and the speed of increasing the records. Some of the reports generated by researchers on the health care systems shows that, one of the health care system alone has reached in 2011, 150 Exabyte. At this rate of increase of growth, in future the bigdata reaches Zettabyte scale and soon it reaches to Yottabyte from various sources like electronic medical records Systems, social media reports, Personal health reports, mobile health care records, analytical reports on large array of biomedical sensors and smart phones. The electronic-health medical reports generated by single patient generates thousands of medical reports which includes medical reports, lab reports, insurances, digital image reports , billing details etc.All these records are needed to be stored in database for validating , integrating these records for meaningful analysis. If these reports are generated by multiple patients across the whole world of healthcare processing system then we have to combine these whole data into a single system which is a big challenge for Big Data. As the volume and Source of storing the data increases rapidly then we can utilize the e-health data to reduce the cost and improves the treatment. We can achieve it by investigating the big data e-health System that satisfies Big Data applications. BIG DATA FOUNDATIONS FOR E-HEALTH : The Following Figure 1 shows the bigdata service environment architecture that provides the support for electronic-health applications from different sources like testing center, individual patients, insurance facilitator and government agencies .All these produces some standard health records are connected commonly to a national healthcare network. Figure 1. e-Health Big Data Service Environments Different types of Data sources : The different types of data sources may include structured database, unstructured datasets and semi structured information Some of the standard structured data that deals with the drug insurance policy by NCPDP (National Council for Prescription Drug Program) and NCPDP SCRIPT for messaging the electronic prescription for validating the interaction between drug to drug, medical database records, dosage of drug, maintain the records. The semi structured data related to radiology pictures are changed over the IP networks is provided by DICOM(Digital Imaging and communication in Medicine). The e-Health system store, gather the medical information, patient information to the doctors unexpectedly includes medical information, vaccination details, diagnostics’ reports. HDWA Healthcare Data Warehousing Association it provides the environment for from others. They work collaboratively which helps them to deliver accurate results or solutions from their own organizations A strong relationship and interaction from test facilitators and technical team is maintained within the organization. We have to face the challenges for utilizing the unstructured data related to different concepts, sharing and accessing the data. Big data solutions and products: Bigdata investigation requires knowledge about storing, inspecting, discovering, visualizing the data and providing security by making some changes to some of technologies like Hadoop, MapReduce, STORM and with combinations. STROM: STROM is a distributed, open source , real time and fault-tolerant computational system. It can process the large amount of data on different machines and in real time each message will be processed. Strom programs can be developed by using any programming languages but especially it uses java , python and other. Strom is extremely fast and has the capability to process millions of records per second per node as it is required for e-health services. It combines with the message queuing and database technologies. From the figure 2 we can observe that a Strom topology takes huge amount of data and process the data in a typical manner and repartitioning the streams of data between each stage of process. A strom topology consists of spout and bolts that can process huge amount of data. In terms of strom components the spout reads the incoming data and it can also read the data from existing files .if the file is modified then spout also enters the modified data also. Bolt is responsible for all processing what happens on the topology , it can do anything from filtering to joins, aggregations, talking to database. Bolts receive the data from spout for processing. Figure 2. Illustration of STORM Architecture (ref: https://storm.apache.org/) Some of the important characteristics of Strom for data processing are: Fast-It can process one million 100 bytes per second per node Scalable-with parallel calculations that runs across the machine Fault-tolerant-if a node dies strom will automatically restart them Reliable-strom can process each unit of data alleast once or exactly once Easy to operate-once deployed strom can be operated easily (ref: http://hortonworks.com/hadoop/storm/) Hadoop for batch processing: Hadoop was initially designed for batch processing i.e., it takes inputs as a large set of data at once, process it and write the output. Through this batch processing and HDFS(hadoop distributed file system) it produce high throughput data processing.Hadoop is another framework , runs on MapReduce technology to do distributed computations on different servers. (ref diagram: http://en.wikipedia.org/wiki/Apache_Hadoop) Figure 3. Hadoop Processing Systems From the figure 3 we can observe that a hadoop multi-node cluster , it consists of single master node and slave node. A master node has different trackers like task tracker for scheduling the tasks , job tracker server handles with the job appointments in a order. Master also acts like a data node and name node. The slave node acts like a task tracker and data node which process the data only by slave-node only. HDFS layer deals with large cluster of nodes manage the name node server which prevents the corruption of file by taking the snapshots of the name node memory structure. Many top companies uses the hadoop technology plays a prominent role in the market.The Vendors who uses Hadoop technology will produce accurate results with high performance, scalability in output and cost is reduced. Some of the companies like Amazon, IBM, Zettaset, Dell and other uses Hadoop technology for easy analysis, provides security, user friendly solutions for complex problems.( http://www.technavio.com/blog/top-14-hadoop-technology-companies) MAPREDUCE: In 2004, Google released a framework called Hadoop MapReduce. This framework is used for writing the applications which process huge amount of multi-terabyte data sets in parallel on large number of nodes. MapReduce divides the work loads into multiple tasks that can be executed parallel. Computational process can be done on both file system and database. (ref: http://en.wikipedia.org/wiki/MapReduce) MapReduce code is usuallay written in java program and it can also can write in another programming languages. It consists of two fundamental components like Map and Reduce. The input and output generated by MapReduce is in the form of key and value pair. The map node will take the input in the form of large clusters and divides it into smaller clusters were the execution process is easy. Rather Mapreduce provides support for hadoop distributed file system can store the data in different servers. This framework provides support for thousands of computational applications and peg bytes of data. Some of the important features of mapreduce are scale-out architecture , security and authentication, resource manager, optimized scheduling, flexibility and high availability. Additional tools are needed to add and should be trained for e-Health files to reduce the complexity because some of the compressed files like electronic-health DICOM picture file should be mapped to a singler Map Reducer so it reduces the BigdData effectiveness. The Hadoop big data applications has imposed a limitations on big data technologies has focused on the applications like offline informatics systems. 4) Programming Tools: The other solution for the e-Health bigdata is MUMPS, it is an programming tool. MUMPS is abbreviated as Massachusetts General Hospital Utility Multi-Programming System. It is also known as M programming language. M is a multi user and it is designed to control the huge amount of database. M programming can produce high performance in health cares and in financial applications. M provides simple data considerations in which the data is given in the form of string of characters and the given data is structured in a multidimensional array. M requires support for sparse data.Accorrding to the research done by the scientist in US hospitals they are maintaing the electronic Health records (HER) using M language including Vista(Veterans Health Information Systems and Technology Architecture) which manages all hospitals care facilities run by the Department of Veterans. (ref: http://opensource.com/health/12/2/join-m-revolution) In future some of the analytical algorithms are developed to solve the problems faced with the big data applications Additional e-Health (Big Data) Capabilities: The additional capabilities provided by the Big data e-Health services are Data Federation and aggregation, Security and Regulatory Concerns and Data Operational Management. The bigdata provides the services which helps to organize and store the huge amount of data. Those data is is digitalization , consists of large amount of datasets consists information related to patients all reports. 1) Data Federation and Aggregation: Data Federation is a type of software which collections the data from the multiple users and integrates the data.Typically traditional software cannot given the solution to store the huge amount of data in hardwares or by some database management tools.But the Data federation will provide a solution based upon the bigdata architecture is based by collecting the data inside and outside of the enterprise through the layer. Some of the important data federation tools are Sysbase federation, IBM InfoSphere Federation server and so on. (ref: http://etl-tools.info/en/data-federation.html) 2) Security and Regularity Concerns: Security is one of the important requirement to describe bidgata e-health services.Security plays a important role because patient share their personl information with the doctors which help the physician to give the correct treatment 3) Data Operational Management

The Life of Charles Babbage :: Free Essay Writer

The Life of Charles Babbage Charles Babbage 1791-1871 Born December 26, 1791 in Teignmouth, Devonshire UK, Charles Babbage was known as the â€Å"Father of Computing† for his contributions to the basic design of the computer through his Analytical Engine. The Analytical Engine was the earliest expression of an all-purpose, programmable computer. His previous Difference Engine was a special purpose device intended for the production of tables. Both the Difference and Analytical Engines were the earliest direct progenitors of modern computers. Even as a little boy, he always tinkered with little mechanical things. He loved to take apart and dissect things. Eventually, Babbage was put in the care of a church school near Exeter, where the minister was told by his family to make sure that he was healthy, rather than well educated. Because of this concern, the minister didn't give Babbage enough work to keep him interested and occupied. Superstitious, despite a thorough Protestant upbringing, he developed an obsession with the Devil. He asked his classmates to tell him every folk tales they knew about what forms the Devil appeared in. In 1812, he began his formal education at Trinity College and the University of Cambridge where he discovered his ability and interest in mathematics history. During that same year, he helped found the Analytical Society, whose object was to introduce developments from the European continent into English mathematics. He graduated from Peterhouse in 1814. He became a fellow of the Royal Society of London in 1816 and was active in the founding of the Royal Astronomical and the Statistical societies. He received his Masters in 1817 and began working as a mathematician, concentrating in calculating functions. It was his work with these complex calculations that led him to his most significant inventions: The Difference Engine and the Analytical Engine. By previous standards, these engines were monumental in conception, size, and complexity. In 1821, Babbage began the task of mechanizing the production of tables. In 1822, he proposed to build a machine called the Difference Engine to automatically calculate mathematical tables. The idea was to invent a calculating machine that could not only calculate without error but also automatically print the results. Difference engines were designed to calculate using the method of finite differences, a well-used principle of the time. It was only partially completed when he conceived the idea of a more sophisticated machine called the Analytical Engine.