Cassius Clay Better Known As Muhammad Ali Is By Far The Greatest Boxer

Cassius Clay better known as Muhammad Ali is by far the greatest boxer of all time. "King of the World" by David Reminick is a very detailed biography of Muhammad and good documentation how boxing used to be. The book takes you on a journey behind the scenes of Allis rise to the top and boxing run in with La Costra Nostra. On an October afternoon in 1954 when Cassius was 12 he left his 60 dollar red Schwinn outside the Columbia Auditorium to visit a bazaar. When he and his friends left he realizes that his new bike was stolen. Cassius was in a tearing rage and someone said that there was a police officer in the basement of a boxing gym. He went in demanding a statewide bike hunt and threatening to beat the hell out of whoever had stolen it. The officer Joe Martin asked Cassius if he could fight, and Cassius said no, so Martin invited him to come to the gym and learn how to box, so he could get pay back on the bicycle thief. This is the story of how Cassius first got interested and determined to become a great boxer. He also showed determinations when he brought home and Olympic gold medal. He trained very hard for our country and did a really good job. Even back then he ran his trashed talked his opponents, like in his first match he fought he one by a spit decision, after he found out he had one he shouted he would soon be "the greatest of all time". Know one knew at the time that his boasts would soon be the truth. Cassius mouth has gotten him a lot of key matches in his career. He gained his first title shot form Sonny Liston this way. One of his famous quotes was "Im so mean I make medicine sick." He ran his mouth so often that people thought he ran his mouth just to psyche him self-out for the matches. That is said to be how he one all of his matches. Before the Liston fight he charted a bus around with signs that read "We all love Cassius Clay", "Without Cassius the game is dead! "March on Listons camp." "BEAR HUNTIN." Cassius first heavy weight title shot was against Sonny Liston a very big man who would give Mike Tyson a good run for his money. There was a lot of hype surrounding their first fight. Cassius ran his mouth so much that everyone thought that Liston was going to kill Clay. Liston actually threatened to kill Clay. The day of the fight the tension in the air was so thick you could cut it with a knife. In the ring before they touched gloves and Cassius told Liston "Ive got you now, sucker!" Cassius knew that he could not match Listons power, so he danced. Liston could land a punch on Clay all night. By the sixth round Liston knew he was going down that night with the quickness. It has been said but not proven that Sonny instructed his assistant to juice his gloves. This is were you rub ferric chloride on a pair of boxing gloves, which is a stinging solution that tends to blind. During the fourth round Cassiuss eyes began to sting. He lost his vision and the sixth round. By the seventh he gained it back and came back with the knock out. He screamed "Eat your words!" Cassius was now world heavy weight champion. A rematch happened between Clay and Liston on May 25, 1965. Very early in the match Cassius knocked out Liston with the infamous "phantom punch". This punch was thrown so quickly that it almost escaped even the eye of the camera. Before the referee counted to ten Cassius was over Liston shouting "Get up and fight, you bum! Youre supposed to be so bad! Nobody will believe this!" Liston was out cold. The crowd shouted "FAKE!" It has never been proven that Liston actually threw the fight. Muhammad was baptized Catholic, but got very interested with The Nation of Islam. In the beginning of his career he had to hide his interests in "The Nation", because he felt that the public was not

Data mining crime of data The WritePass Journal

Data mining crime of data 2  LITERATURE REVIEW Data mining crime of data 1  Ã‚  INTRODUCTION1.1   Ã‚  MOTIVATION AND BACKGROUND 1.2 PROBLEM DEFINITION1.3 PROJECT GOAL1.4 GENERAL APPROACH1.5   ORGANISATION OF DOCUMENT2  LITERATURE REVIEW2.1 INTRODUCTIO2.2  Ã‚  What is Data mining?2.3 Data mining definitions2.4 Data mining structure 2.5 Data mining methods and Techniques 2.6 Data mining modellingREFERENCERelated 1  Ã‚  INTRODUCTION 1.1   Ã‚  MOTIVATION AND BACKGROUND In the society crime issue is very important. It is common knowledge within the society that crime induces vast psychological, human and economical damages to individuals, environment and the economy of a particular society itself. It is very important that a society through its government, judiciary and legislative endeavours to control crime within their environment. Data mining is a brawny technique with expectant potency to help criminal investigators concentrate on the most important information in their crime data [1]. The cognition discovered from existing data goes to reveal a value added of its information. Successful data mining methodology depends intemperately on the peculiar selection of techniques employed by analyst. Their pragmatic applications are, for example, the criminal detectives, market sale forecasting and playing behaviour analysis in sport games. However the more the data and more composite question being treated and maintained, the more potent the system is required. This includes the potentiality of the system to analyze large quantity of data and composite information from various sources. In crime control of the law enforcement, there are many storage data and formats have to be revealed. When its amount has risen, it is hard to analyze and explore some new knowledge from them. Therefore, the data mining is employed to crime control and criminal curtailment by using frequency happening and length method under which these presumptions can be achieved. All these techniques give outcome to benefit detectives in searching behavioural practices of professional criminals. [1] The application in the law enforcement from data analysis can be categorised into two vital component, crime check and criminal curtailment. Crime check tends to use knowledge from the analysed data to control and prevent the happening of crime, while the criminal curtailment tries to arrest criminal by using his/her account records in data mining. Brown [2] has bui lt a software model for mining data in order to arrest professional criminals. They suggested an information system that can be used to apprehend criminals in their own area or regions.   The software can be employed to turn data into useful information with two technologies, data fusion and data mining. Data fusion deals fuses and translates information from multiple sources, and it overcomes confusion from conflict reports and cluttered or noisy backgrounds. Data mining is concerned with the automatic discovery of patterns and relationships in large databases. His software is called ReCAP(Regional Crime Analysis Program), which was built to provide crime analysis with both technologies (). When the terrorism was burst by 9/11 attacks, fears about national security has risen significantly and the world has varied forever. The strategy against a terrorist must be more advanced in order to prevent suicide attacks from their stratagem [5]. In the congressional conference, Robert S. Mueller – The Director of investigative department of FBI, suggested that they excessively emphasize to arrest the criminals with slightly attention for crime checks is the main problem of the law enforcement in the world [4].   It is more interesting now in data collection for criminal control plan. Abraham et. al. [1] suggested a method to use computer log register as account data search, some relationship by employing the frequency happening of incidents. Then, they analyze the outcome of produced profiles. The profiles could be employed to comprehend the behaviour of criminal. It should be observe that the types of crime could be exchanged over time determined by the variation of globalization and technology. Therefore, if we want to prevent crime efficiently, the behaviour must be employed with another kind of knowledge. We need to know the crime causes. de Bruin et. al. [3] introduced a new distance standard for comparing all individuals established on their profiles and then clustering them accordingly. This method concedes a visual clustering of criminal career and changes the identification of categories of criminals. They present the applicability of the data mining in the area of criminal career analysis. Four important elements play a role in the analysis of criminal career: crime nature, frequency, duration and severity. They also develop a particular distance standard to combine this profile difference with crime frequency and vary of criminal behaviour over time. The matrix was made that describe the number of variation in criminal careers between all couples of culprits. The data analysis can be employed to determine the trends of criminal careers. Nath[6] suggested a method for data division in order to use them present in the pattern of geographic map. We could decide the data division to be the veer of offend across many fields. 1.2 PROBLEM DEFINITION The report of the headline findings represents the 2006 Offending, crime and justice survey (OCJS). This gives description of style and degrees in youth offending anti-social behaviour (ASB) and victimisation amongst youth between the ages of 10-25 residing in a private household in England and Wales. Couple of years now data are obtained from respondent representatives’ interview 4,952 including 4,152 panel members and 799 fresh samples on the frequency consequences and characteristics of offender’s victimization in England and Wales. This survey enables the Offending, crime and justice survey (OCJS) to forecast the probability of specified outcome of victimization by assault, rape, theft, robbery, burglary, sexual assault, vehicle related theft, drug selling, for the population as a whole. The OCJS provides the largest forum in England and Wales for victims and offenders to describe the impact of crime and characteristics of offenders. 1.3 PROJECT GOAL This project aim to identify which of the data mining technique best suit the OCJS data. Identify underlying classes of offenders. 1.4 GENERAL APPROACH Data mining analysis has the tendency to work from the data up and the best techniques are those developed with a preference for large amount of data, making use of as much of the gathered data as potential to arrive at a reliable decision and conclusion. The analysis procedure starts with a set of data, employs a methodology to develop an optimal delegacy of the structure of the data during which time knowledge is gained. Once knowledge has been gained this can be broadened to large sets of data working on the effrontery that the larger the data set has a structure similar to the sample data.   Again this is analogous to a mining process where large numbers of low grade materials are sieved through in order to find something of value. Target Data Figure 1.1 Stages/Procedures identified in data mining adapted from [4]       1.5   ORGANISATION OF DOCUMENT The remainder of this report is as follow: Chapter 2 reviews the approach to data mining and also described the mining techniques. Chapter 3 introduced the basic theory for the algorithm. Chapter 4 described the adopted method. Chapter 5 presented the application and a discussion of the result. 2  LITERATURE REVIEW 2.1 INTRODUCTIO The major reason that data mining has pulled a big deal of attention in information industry in recent years is due to the broad accessibility of vast amount of data and the impending need for turning such data into useful information and knowledge. The information and knowledge acquired can be employed for application ranging from business management, production control, and market analysis, to engineering design and science exploration. [4] Having focused so much attention on the collection of data the problem was what to do with this valuable resource? It was distinguished that information is at the centre of business operations and that decision makers could make use of the data stored to acquire valuable insight into business. Database management systems gave access to the data stored but this was only a small part of what could be acquired from the data. Traditional online transaction processing systems, OLTPs, are good at putting data into database quickly, safely and efficien tly but are not good at delivering meaningful analysis in return. Analyzing data can provide further knowledge about a business by going beyond the data explicitly stored to derive knowledge about the business. This is where data mining or knowledge discovery in database (KDD) has obvious benefit for any enterprise. [7]                                                                                    2.2  Ã‚  What is Data mining? Data mining is concerned with extracting or â€Å"mining† knowledge from large amount of data. The term is really misnomer. Recall that the mining of gold from rocks and sand is concerned with gold mining rather than sand and rocks mining. Thus â€Å"data mining† should have been more suitably named â€Å"knowledge mining from data†, which is unfortunately so long. â€Å"Knowledge mining† a shorter term might not show the emphasis on mining from large amount of data.[4,6] Nevertheless, mining is a bright term characterising the procedure that discovers a small set of treasured pearl from a large conduct of raw materials (figure 1). Thus, such Fig.2.1 data mining-searching for knowledge (interesting patterns) in your data. [4] Misnomer which contains both â€Å"data† and â€Å"mining† became a big choice. There are many other terms containing a similar or slightly dissimilar meaning to data mining, such as data archaeology, knowledge extraction, data/ pattern analysis, and data dredging knowledge mining from database. Lots of people treat data mining as an equivalent word for another popular used condition, â€Å"knowledge discovery in database† or KDD. Alternatively, others regard data mining as just an essential step in the procedure of knowledge discovery in database. [2, 4] Knowledge discovery as a process is described in fig.2 below and comprises of an iterative sequence of the following steps: Data cleaning (removal of noise or irrelevant data) Data integration (where product data source may be mixed) Data selection (where data applicable to the analysis task are recovered from the database) Data transformation (where data are translated or fused into forms appropriate for mining by doing summary or collection operations, for instance) Data mining (an essential procedure where intelligent methods are used in other to extract data forms or patterns), Pattern evaluation (to discover the truly concerning forms or patterns representing knowledge based on interest measure) Knowledge representation (where visualization and knowledge representation proficiencies are used to deliver the mined knowledge to the user) Fig.2.2 Data mining as a process of knowledge discovery adapted from [4, 7] The data mining steps may interact with the user or a knowledge base. The interesting patterns are presented to the user, and may be stored as new knowledge in the knowledge base. It is very important to note that according to this view, data mining is only one step in the entire process, albeit an essential one since it uncovers the hidden patterns for evaluation. Adopting a broad view of data mining functionality, data mining is the process of discovery interesting knowledge from large amount of data stored either in database, data warehouse, or other information repositories. Based on this view, the architecture of a typical data mining system may have the following major components: (1)   Data warehouse, database, or other information repository. This is one or a set of database, data warehouse, spread sheets, or other kind f information repositories. Data cleaning and data integration techniques may be performed on the data. (2)   Database or data warehouse server. The database or data warehouse server is responsible for fetching the relevant data, base on the user’s data mining request. (3)   Knowledge base. This is the domain knowledge that is used to guide the search, or evaluate the interestingness of resulting patterns. Such knowledge can include concept hierarchies, used to organize attributes or attributes values into different level of abstraction. Knowledge such as user beliefs, which can be used to assess a pattern’s interestingness based on its unexpectedness, may also be included. Other examples of domain knowledge are additional interestingness constraints or thresholds, and metadata (e.g., describing data from multiple heterogeneous sources). (4    Data mining engine. This is essential to data mining system and ideally consists of a set of functional module for tasks such as characterisation, association analysis, classification, evaluation and deviation analysis. (5)    Pattern evaluation module. This component typically employs interestingness measure and interacts with the data mining modules so as to focus the search towards interesting patterns. It may access interestingness threshold stored in the knowledge base. Alternatively the pattern evaluation module may be integrated with the mining module, depending on the implementation of the data mining method used. For efficient data mining, it is highly recommended to push evaluation of patterns interestingness as deep as possible into the mining process so as to confine the search to only the interesting patterns. (6)    Graphical user interface. This module communicate between the user and the data mining system, allowing the user to interact with the system by specifying a data mining query or task, providing information to help focus the search, and   performing exploratory data mining based on the intermediate data mining results. In evaluate mined patterns, and visualize the pattern in different forms.[4, 6, 7] The quantity of data continues to increase at a tremendous rate even though the data stores are already huge. The common problem is how to make the database a competitive job advantage by changing apparently meaningless data into useful information. How this challenge is satisfied is vital because institutions are increasingly banking on efficient analysis of the data simply to remain competitive. A variety of new techniques and technology is rising to assist sort through the information and discover useful competitive data. By knowledge discovery in databases, interesting knowledge, regularities, or high-ranking information can be elicited from the applicable set of information in databases and be looked-into from different angles; large databases thereby assist as ample and dependable source for knowledge generation and confirmations. Mining information and knowledge from large database has been recognized by many research workers as a key research subject in database systems and m achine learning. Institutions in many industries also take knowledge discovery as an important area with a chance of major revenue. [8] The discovered knowledge can be applied to information management, query processing, decision making, process control, and many other applications. From data warehouse view, data mining can be considered as an advance stage of on-line analytical processing (OLAP). However, data mining extends far beyond the constrict measure summarization mode analytical processing of data warehousing systems by integrating more advance techniques for information understanding [6, 8]. Many individuals treat data mining as an equivalent word for another popularly applied condition, Knowledge Discovery in Databases, or KDD. Alternatively, others view data mining as simply an indispensable measure in the process of knowledge discovery in databases. For example, the KDD process as follow: Learning the application domain Creating a dataset Data cleaning and pre-processing Data reduction and projection Choosing the function of data mining Choosing the data mining algorithm(s) Data mining Interpretation Using the discovery knowledge As the KDD process shows, data mining is the fundamental of knowledge discovering, it needs elaborated data training works. Data cleaning and pre-processing: includes basic operations such as removing noise or outliers, gathering the necessary data to model or account for noise, resolving on strategies for dealing with missing data fields, and accounting for time sequence data and recognised changes, as well as settling DBMS issues, such as mapping of missing and unknown values, information type, and outline. Useful data are selected from the arranged data to increase the potency and focus on the job. After data preparation, selecting the purpose of data mining determine the aim of the model gained by data mining algorithm (e.g. clustering, classification and summarization). Selecting the data mining algorithm includes choosing method(s) to be used for researching for patterns in the data, such as determining which models and parameters many are captured and corresponding to a particular data mining method with the overall standards of the KDD process. Data mining explores for patterns concern in a particular realistic form set of such representations; including classification rules, or clustering, regression, sequence modelling, trees, dependency and line analysis. The mining outcomes which correspond to the demands will be translated and mobilised, to be taken into process or be introduced to concerned companies in the last step. For the importance of data mining in KDD process, the term data mining is turning more popular than the longer term of knowledge discovery [3, 8]. Individuals gradually conform a broad opinion of data mining functionality to be the equivalent word of KDD. The concept of data mining holds all actions and techniques using the gathered data to get inexplicit information and studying historical records to acquire valuable knowledge. 2.3 Data mining definitions Larose [9] stipulated, data mining refers to the process of discovering meaningful new correlations, patterns and trends by sifting through large amount of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques. Hand et. al.[10] stated Data mining the analysis of (often large ) observational data sets to find unexpected relationship and to summarize the data in novel way that are both understandable and useful to the data owner. Peter et.al.[11] stipulated Data mining is an interdisciplinary field bringing together techniques from machine learning, pattern recognition, statistics, databases, and visualization to address the issues of information extraction from large data bases. The SAS institute (2000) defines data mining as the â€Å"process of selecting, researching and simulating huge amount of data set to reveal antecedent strange data form for business advantage. Data mining refers to as knowledge discovery in dat abases, meaning a process of little extraction of implicit, previously obscure and potentially useful information (such as knowledge rules, regularities, constraints) from data in databases.[12] From the business view, several data mining techniques are used to better realize user conduct, to improve the service provided, and to enhance business opportunities. Whatever the definition, data mining process differs, from statistical analysis of data. First predictive data is controlled by the need to reveal, in a well timed style, rising courses whereas statistical analysis is associated to historical information and established on observed information. Secondly statistical analysis concentrates on findings and explaining the major origin of variation in the data, in contrast, data mining strives to discover, not the apparent sources of variation, but rather the significant, although presently neglected, information. Therefore statistical analysis and data mining are complementary. Sta tistical analysis explains and gets rid of the major part of data variation before data mining is employed. This explains why the data warehousing tool not only stores data but also comprises and performs some statistical analysis programs. As to on-line analysis processing (OLAP) its relationship with data mining can be considered as disassociation.[9,12] OLAP is a data summarization/collection tool that assist modify data analysis, while data mining allows the automated discovery of implicit form and interesting knowledge concealed in large amount of data. OLAP tools are directed toward backing and changing interactive data analysis; while data mining tools is to automate as much of the analysis process as possible. Data mining goes one step beyond OLAP. As noted in the former section, data mining is almost equal to KDD and they have like process. Below are the data mining processes: Human resource identification Problem specification Data prospecting Domain knowledge elicitation Methodology identification Data pre-processing Pattern discovery Knowledge post-processing In stage 1 of data mining process, human resource identification, and the human resource should be required in the plan and their various purpose are identified. In most data mining job the human resources involved are the field experts, the data experts, and the data mining expert. In stage 2 concerned jobs are analyzed and defined. Next, data searching requires in analyzing the available data and selecting the predicting subset of data to mine. The aim of stage 4, field knowledge induction, is to extract the useful knowledge already known about the job from field experts. In stage 5, methodology identification, the most reserve mining prototypes are chosen. In stage 6, data pre-processing is depicted to transform data into the state fit for mining. Pattern discovery stage which includes the computation and knowledge discovery is talked about in section 7. The patterns found in the former stage are filtered to attract the best pattern in the last stage. [8] Fayed et al. (1996), on the other hand suggested the following steps: Recovering the data from large database. Choosing the applicable subset to work with. Resolving on reserve sampling system, cleaning the data and coping with missing domain records. Employing applicable transformations, dimensionality simplification and projections. Equipping models to pre-processed data. The processes of data mining are elaborate and complicated. Many requirements should be observed on the follow of data mining, so challenges of growing data mining application are one of the crucial matters in this field. Below are the listed of challenges growth: Dealing with different types of data. Efficiency and measurability of data mining algorithm. Usefulness, certainty, and quality of data mining results Formula of several forms of data mining results. Synergistic mining knowledge at product abstraction stages. Mining data from different sources of information. Security of privacy and data protection. 2.4 Data mining structure The architecture of a distinctive data mining system may have the following major elements: Database, data warehouse, or other data deposit Database or data warehouse server Knowledge base Data mining engine Pattern rating module Graphical user interface The information sources of a data mining system can be divergent information deposits like database, data warehouse, or other deposits. The database or data warehouse server is responsible for getting the applicable data to accomplish the data mining postulation. Data mining engine is the heart of data mining system. The operational module of data mining algorithms and patterns are maintained in the engine. Knowledge base stores the field knowledge that is used to lead the data mining process, and provides the data that rules evaluation module motives to formalise the results of data mining. If the mining results has passed the establishment step then they will get to user via the graphical user interface, user can interact with the system by the interface. [4, 8, 11] 2.5 Data mining methods and Techniques Various techniques have been suggested for resolving a problem of extracting knowledge from volatile data, each of which follow different algorithm. One of the fields where information plays an important part is that of law enforcement. Obviously, the raising amount of criminal data gives rise to various problem including data storage, data warehousing and data analysis. [11] Data mining methods relate to the function cases that data mining tools provides. The abstract definition of each data mining method and the classification basis always disagree for the ease of explanation, the condition of present situation, or researcher’s scope. Association, classification, prediction, clustering are usually the common methods in different works, while the term description, summarization, sequential rules etc. Might not always be used and named in the first place. If some methods are not named it does not refer these methods are not created because the researcher may allot special term to methods to indicate certain significant characters. Progressive specification and jobs sectors can also be a good ground to consider the terminology. For example â€Å"REGRESSION† is often used to substitute â€Å"PREDICTION† because the major and conventional techniques for prediction are statistical regression. â€Å"Link analysis† can be discussed severally outlying â€Å"association† in telecommunication industry. Table 1.1 shows the method recognised by scholars: Data mining methods comprise techniques which develop from artificial intelligence, statistics, machine learning, OLAP and so on. These most often observed methods are classed into five categories according to their work types in business applications, and the five types of data mining methods are clustering, classification, association, prediction and profiling. Table 1 Data mining classification literatures Sources: This research   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Author   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Data Mining Classification Barry (1997) Prediction, Classification, Estimation, Clustering, Description, Affinity grouping. Han, et al. (1996) Clustering, Association, Classification, Generalization, Similarity search, Path traversal pattern. Fayyed, et (1996) Clustering, Regression, Classification, Summarization, Dependency modelling, Link analysis, Sequence analysis. Association: reveals relationship or dependence between multiple things, such as Link analysis, market basket analysis, and variable dependency. Association is in two levels: quantitative and structured. The structural level of the method assigns (often in graphical form) which things are associated; the quantitative level assigns the strength of the relationship using some numerical scale. Market basket analysis is a well recognized association application; it can be executed on a retail data of customer deal to find out what item are often purchased together (also known as item sets). Apriori is a basic algorithm for finding frequent item sets. The denotation of apriori can further deal with multi-level, multi-dimensional and more composite data structure. [7] Classification: function (or classifies) a data detail into one of several set of categorical classes. Neural network, Decision tree, and some probability advances are often used to execute this function. There are two steps to carry out classification work. In the first step, classification model is form describing a predetermined set up of classes or concepts. Second step the model is used for categorization. For example, the classification learned in the first step from the analysis of data from existing customers can be used to predict the credit evaluation of new or future customers. Prediction: admits regression and part of time series analysis. Prediction can be regarded as the structure and use of a model to evaluate the value or value ranges of a property that a given sample is probably to have. This method functions a data item to a real-value prediction variable, and the goal of time series analysis is to model the state of the process generating the sequence or to extract and study deviation and style over time. In our opinion, the major deviation between prediction and classification is that prediction processes with continuous values while classification centres on judgements. Clustering: functions a data item into one of various categorical classes (or clusters) in which the categories must be determined from the data different assortments in which the classes are determine. Clusters are defined by determinations of natural grouping of data detail based on similarities metrics or probability density models, and the procedure to form these groups is named as unsupervised learning to distinguish from supervised learning of classification. Data mining techniques and methods render capable extraction of concealed predictive data from huge datasets or databases. It is a very powerful new technology with great potency to assist institutions concentrate on the important information in their database and data warehouse. Data mining instruments forecast future behaviours and trends, allowing businesses to make active, knowledge aimed decision. The automated, potential analyses proposed by data mining go beyond the analyses of previous issues provided by retrospective instruments typically for decision support systems. [2, 4, 12] Data mining instruments can respond to business question that traditionally were times consuming to conclude. They examine databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectation. Most institutions already collect and refine large quantities of data. Data mining methods and techniques can be carried out quickly on existing hardware and software program to raise the scope of the existing information resources, and can be merged with new systems and products as they are brought on-line. When enforced on high performance client/server or parallel processing computers, data mining instrument can analyze huge databases to present answers to questions such as, which clients are most likely to answer my next promotional mailing, and why?[10, 12] Recent progress in data collection, storage and manipulation instruments such as extraordinary storage and computational capability, use of the internet, modern surveillance equipments etc, have widen the range and limit for the same. Moreover, the increasing dependence on high technology equipment for common man has facilitated the process of data collection. [13] The data might be in the direct form or may not be in the direct form and might need some interpretation based on former knowledge, experience and most importantly is determined by purpose of data analyses. This job is further augmented by sheer intensity, texture of data and lack of human capabilities to deduce it in ways it is supposed to be. For this reason many computational instruments are used and are broadly named as Data mining tools. [10] Data mining tools constitutes of basic statistics and Regression methods, Decision trees, ANOVA and rule based techniques and more importantly advanced algorithm that uses neural networks and Artificial Intelligence techniques. The applications of data mining tools are limitless and basically aimed by cost, time constraints, and current demand of the community, business and the government. [14] 2.6 Data mining modelling Data mining modelling is the critical part in developing business applications. Business application, such as â€Å"cross selling†, will be turn into one or more of business problems, and the goal of modelling is to formulate these business problems as a data mining task. For example, in cross selling application, the association in the product area is determine, and then customers will be classified into several clusters to see which product mix can be matched to what customers. To know which data mining task is most suitable for current problem, the analysis and understanding of data mining task’s characters and steps is needed. Data mining algorithm consists largely of some specific mix of three components. The model: There are two relevant factors: the function of the model (e.g., clustering and classification) and the representational form of the model (e.g., a linear function of multiple variables and a Gaussian probability density function). A model contains parameters that are to be determined from the data. The preference criterion: A basis preference of one model or set of parameters over another, depending on given data. The criterion is usually some form of goodness-of-fit function of the model to the data, perhaps tempered by a smoothing term to avoid over fitting, or generating a model with too many degrees of freedom to be constrained by the given data. The search algorithm: The specification of an algorithm for finding particular models and parameters, given data, a model (or family of models), and a preference criterion. The choice of what data mining techniques to apply at a given point in the knowledge discovery processes depends on the particular data mining task to be accomplished and on the data available for analysis. The requirement of the task dedicate to the function f data mining, and the detailed characteristics of the tasks influence the feasibility between mining methods and business problems. The so called detail characteristic includes data types, parameter varieties, hybrid approaches and so on.   Slightly difference in the model will cause enormous performance change, so modelling stage effects the quality of data mining tools heavily. REFERENCE [1] T. Abraham and O. de Vel, Investigative profiling with computer forensic log data and association rules, in Proceedings of the IEEE International Conference on Data Mining (ICDM02), pp. 11 – 18,2006. [2] D.E. Brown, The regional crime analysis program (RECAP): A frame work for mining data to catch criminals, in Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics,Vol. 3, pp. 2848-2853, 1998. [3] J.S. de Bruin, T.K. Cocx, W.A. Kosters, J. Laros and J.N. Kok, â€Å"Data mining approaches to criminal career analysis,† in Proceedings of the Sixth International Conference on Data Mining (ICDM’06), pp.171-177, 2006. [4] J. Han and M. Kamber, â€Å"Data Mining: Concepts and Techniques,†Morgan Kaufmann publications, pp. 1-39, 2006. [5] J. Mena, â€Å"Investigative Data Mining for Security and Criminal Detection†, Butterworth Heinemann Press, pp. 15-16, 2003. [6] S.V. Nath, â€Å"Crime pattern detection using data mining,† in Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pp. 41-44,2006. [7] S. Nagabhushana â€Å"Data warehousing OLAP and Data mining†, published by new age international,pp251-350, 2006 [8]Takao Terano, Huan Liu, Arbee L.P. Chen (Eds.) 2000 â€Å"knowledge discovery and data mining current issues and application† [9]Larose, Daniel T. 2005 Discovering Knowledge in data mining. An introduction to data mining (pg.3) [10]Hand, D. J.Heikki Mannile, padhraic Smyth, 2001 Principle of data mining (pg. 1) [11] Peter cabena, Pablo Hadjinian, Rolf stadler, Jaap verhees, and alessandro zanasi, 1998 [12]Eric D. Kolaczyk 2009 Statistical analysis of network data, method and models Discovering data mining: from concept to implementation† (pg. 2) [13]Trevor Hastic, Robert   Tibshirani, Jerome freedman 2001 The Element of Statistical learning, data mining, inference, and Prediction [14] An Introduction to Data Mining: thearling.com/text/dmwhite/dmwhite.htm (Internet site accessed on 27th April 2011.) [15] Padhye, Manoday Dhananjay â€Å"Use of Data Mining for Investigation of Crime Patterns†, [16]Graham J. williams, simeon J. simmoff (edu).2006 Data mining: Theory, methodology, Techniques, and applications [17]Hill Kargupta, Jiawei Han, Philip S. Yu, Rajeev Motwani and Vipin Kumar 2009 Next generation of data mining [18]Robert G. Cowell, A. Philip David, Steffen L. Lauritzen, David J. Spieglhalter 1999 Statistics for Engineering and Information science [19]Deepayan Sarkar, 2008 Multivariate data Visualization with R. [20]Luis Torgo2011 data mining with R. learning with case studies [21] Everitt, Brian and Graham Dunn 2001 â€Å"Applied multivariate data analysis† Masters Thesis,West VirginiaUniversity. 2006. P. Thongtae and S. Srisuk An Analysis of Data Mining Applications in Crime Domain Computer and Information Technology Workshops, 2008. CIT Workshops 2008. IEEE 8th International Conference on Tabachnick, Barbara G., 1936- Using multivariate statistics / Barbara G. Tabachnick, Linda S. Fidell . 5th ed. . -Boston,Mass.;London: Pearson : Allyn and Bacon, 2007 . 0205465250

Nursing research Essay Example | Topics and Well Written Essays - 250 words - 11

Nursing research - Essay Example In addition, it can also be determined by whether it has measured what it is intended to measure in this case the measure being the face value of data, the content validity of the data, and  by  a panel of judges. In this case, the judges use their opinion to determine whether the tool measured its conceived measure (Wood & Ross-Kerr, 2010). On the other hand, the validity of an instrument can also be determined using pragmatic measures that test the practical value of an instrument while focusing on the research questions. With constant determination of a tool or instrument as valid, there is no need for a researcher to test the reliability of an instrument. Nonetheless, there are three ways to determine the testing of the reliability of an instrument. First, reliability can be determined through testing the stability of the tools by producing dependable results overtime. On the other hand, the second approach involves the test for equivalency. A test for equivalency involves te sting whether the instrument was consistent while used by independent researchers. Finally, the test for reliability may involve testing the internal consistency of a tool by determining whether the measure of the consistency of the tool is in all parts (Wood & Ross-Kerr,

Studies about Hand-raising for Kindergarteners Essay

Studies about Hand-raising for Kindergarteners - Essay Example Here, we review three articles relating to hand-raising with respect to behavioral training. In an early study, investigators used a class of twenty four, 9 to 12 year old behaviorally challenged students (21 male, three female) at the Garfield School in Salt Lake City, Utah (Greenwood, C. R., Sloane, H. N., Jr., & Baskin, A., 1974). A training procedure and two maintenance contingencies on consequence dispensing behavior were explored. Four peer behavior managers were trained to supervise four to six subjects each to work in programmed math materials. Their behavior was compared with a teacher skilled in the use of social and point reinforcement and response cost. A component of many of the appropriate behaviors was hand-raising. The training was partially effective in increasing rates of appropriate social and point dispensing behaviors in managers. Manager reinforcement contingent consequence-dispensing behavior in managers resulted in moderately higher rates of appropriate social and point dispensing behavior for three of four subjects than did having manager reinforcem ent contingent upon group study behavior. Two managers exposed to the group performance consequence before the manager performance consequence increased inappropriate social and point-dispensing behaviors to pre-training base levels. ... It included visual reminders, goal evaluation, positive reinforcement, and constructive feedback at regular intervals. Outcome of three measures of peripheral variables (direct observations of hand raising frequency and talking out of turn during the group sessions and Conners conduct problem ratings). The cueing procedure resulted in significant and robust improvement in two of the three peripheral measures (hand raising frequency ES = 2.73; talking out of turn ES = 2.89). This underscores the benefit of using a theoretical framework for guiding the design and evaluation of therapeutic interventions for children with ADHD. At a professional conference, researchers presented the case studies of two individuals. Jim is a ninth grade bilingual student who has been referred for special education services due to his poor academic performance (Miller, K., Koury, K., Mitchem, K., Fitzgerald, G., & Hollingsead, C., 2005). One of the target behaviors selected for improvement was the raising of the hand and waiting for permission to speak. As part of the intervention, Jim used a self monitoring point card system from KidTools to track his behavior in class. At a timer that sounded every five minutes, Jim recorded his behavior as on-task or off-task. At the end of the week, Jim earned points to purchase items while completing a reinforcement inventory if he was "on-task" for an average of at least 80% of the momentary time sampling opportunities. During the first three days of the intervention, Jim went from being off task on five time sampling observations to being 100% on-task. As reported by his teacher, the i ntervention was very successful as the occurrence of off-task behavior decreased and the student's on-task behavior

Answer four questions Essay Example | Topics and Well Written Essays - 1500 words

Answer four questions - Essay Example On the other hand, management accounting innovation is the adoption of new ideas or modern forms of management accounting systems. Management accounting innovations are among the central themes driving modern organisations. These modern organisations manage to prosper and retain its success in the aggressive market environments through stable innovations towards organisational prosperity. This paper will outline the contribution of management accounting innovations towards organisational success. 1. Why Management Accounting Innovation is one of the core themes driving modern organisations Innovations are of many types, and research suggests that distinguishing the difference between them is very essential because innovations have different attributes (Schmeisser, 2010). More so, the adoption processes of innovations are not the same and factors affecting them differ. There are different types of innovation that mainly are technical innovation, administrative innovation, process inno vation, product innovation, radical innovation and incremental innovation. To start with, technical innovation relates to the major work activities that are carried out in an organisation, while administrative innovation relates to the organisational structure and administrative processes inclusive of the management. Thirdly, process innovation contains an organisation’s process in new elements. ... Innovations vary differently in different organisations due to the sise and activities of an organisation. However, in management accounting only two innovations are commonly used. These two innovations are administrative and radical innovations. 2. Management accounting is the core theme in driving innovation in modern organisations In the past decades, management accounting strategies included both decision-making and analysis (Emsley, 2005). These past management strategies are claimed to be the predecessor for the emerging innovation and the latest technologies. The modern accounting represents both the operational and the financial planning and control. Managerial accounting is a very essential tool in an organisation because it provides essential data with which the organisation operates. In other words, managerial accounting can be simply referred to as cost accounting. The management accountants have the role of preparing reports that focus on how well or bad managers and the business unit have performed (Lucey, 2003). The management accountants go ahead to measure these performance measures and the results are compared to plans and benchmarks. Most of these reports provide frequent updates on essential indicators and any arising problem is addressed. The main problems that arise in the reporting field are declining in profitability, global market crisis and other emerging problems. These problems are then solved strategically. Therefore, management accounting analyses the past, present and the future of an organisation’s performance through financial transactions. These summarised outputs are essential in planning the current and future stability of an organisation through

Functions Of Database Management System

Functions Of Database Management System Before we start with DBMS we should know what is data. A data is a piece of information, and database is the collection of data that is set in an orderly way. And managing this database is known as Database Management System in short DBMS. The person who manages, creates, controls and maintain this database management system is known as Database Administrator (DBA). Another important term to remember is information. Any data which has been converted to a useful and understandable form is called information. There are some differences between data and information. Data Information Any raw figure or fact is data. For example 6 is a data. A processed form of data is known as information. For example weight = 6 kg is data Data does not help in making decisions. With right information we can make decision. Functions of Database Management System Data Modeling: The structured definition of data storage is known as data modeling. Processing Query: This a mechanism of manipulating the data Concurrency Control: To ensure the accuracy and simultaneous access of the database by multiple users. Security of Information: Security of the database is very important. Crash Recovery: Data recovery after the system crashes. Types of Database users Database Administrator (DBA): The database administrator is the person who who maintains designs and creates the database. Database Designer: A database designer is a person who plans or designs the database. End User: The one who uses the database, it may be that he/she only views the database or it may be that he/she makes the data entries. Make queries, etc There may be different types of end user, for example: Sophisticated: these are the users who has a good knowledge in database and can make queries, with SQL manipulate data with DML (Data Manipulating Language) Specialized: who makes application programs that interacts with the database Native: only interacts with the database via some sophisticated programs Application Programmers: A person who makes applications which interacts with the database using programming language like C++ , Java, etc. He may create a software which gives a easy to use interface to input data into a complex database, so that even the clerks of the office can input data or search for some data from the companies database without knowing Oracle, MySQL or other DBMS software Database Model Database Model is the structure or the format of the data; it may be physical or conceptual. Database Model is also known as database schema. Conceptual Model: Conceptual Model helps to overview the organizational schema rather than the database schema. Physical Model: Physical Model is the database design which means that this model describes the data storage, data structure, etc. basically we get to know about the physical media of the data storage and the mode of access of that data from this model. Frame Memory Model: This type of model is generally used for large manufacturing database application. Modifying the characteristic of the complex database easily and accurately. Unifying Model: in the Unifying Model the Entity-Relation concept has been extended to introduce a new form of diagrammatic representation other than class diagrams. Object Oriented Model: A Object Oriented Model is a data model in which the real life data or entities are organized. Generally Object Oriented Data Model or OODM consists of the following concepts, they are as follows: Object and object identifier Attributes and methods Class Class hierarchy and inheritance Record Based Model: The Record Based Model helps us to specify the overall logical structure of the database. In this type of data bases the numbers of types of data are fixed. And each of the record type or data type has a fixed number of fields with fixed field length. There are three types of record based data model they are: Hierarchical Model: In a Hierarchical Model the data is organized in a form of tree like structure. In this kind of structure the parent à ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬ child relationship can easily be shown. A very famous use of this kind of database is the Windows Registry developed by Microsoft. 320px-Hierarchical_Model.jpg Network Model: This is a type of database model where it is easy to represent objects and relationship. Its more easier to define many to many relation in this model rather than in the hierarchical model. A well known implementation of Network Model is RDM Server. 320px-Network_Model.jpg Relational Model: The relational model was developed by E.F Codd. The properties of a relational database model are as follows: The columns of table are all homogenous i.e. they are of the same kind. Every item should have simple value. All the relationship of tuples must be distinct. The key value should be used to order the tuples within a relationship. Columns are named distinctly and their ordering is not so important. 280px-Relational_Model_2.jpg Manual Database. A Manual Database is a record kept by a human without the use of any computers or electronic devices. This obviously has many problems like: Searching: It is very difficult to find a particular result from a manual database if the size of the database is huge. Updating: Updating a new entry is also problematic as we have to manually find the old record, scratch it, or erase it then make the new entry. Backing up: Suppose there is a database of 10,000 pages making a manual backup of this database, i.e. a handwritten copy of it will be difficult to make. Sorting: It is virtually impossible to sort the data say names of customers by alphabetic order, etc on a manual database. As we see that making a manual database of a large amount of data becomes impossible to manage so now we use computerized data. Traditional File Processing System One of the earliest forms of computerizing data storage is the file processing system. Creating, sorting, organizing and accessing the content of the file is known as File Processing System. Characteristics of File Processing System. Each file is different from each other. This is a collection of files, or sorted data. Each of the file is called a flat file. Every file contains processed information of a specific function such as one file may be for accounting other file may be of contacts. Files are created by the help of program which are written in C, C++ or COBOL. Drawbacks of the File Processing System. There are many drawbacks of File Processing System. Separated Data Duplicated Data Data Dependency Data inflexibility Problems in representing the data to user. File format problems. Database: A database is a organized form of data. This organization is very important because when the size of data increases it becomes difficult to use or control the data. Database Management System: A DBMS or Database Management System is a collection of data and programs which help us to access and modify those data. The collection of data is called database. The main purpose of DBMS is to efficiently store and control the database. Advantages of DBMS Control Redundancy: With the help of DBMS data redundancy can be controlled. In the File Processing System there used to be data redundancy, which means that the same data stored more than once. Integrity: Maintaining Integrity means that the data stored in the database is accurate and precise. This is very much important as incorrect data can not be stored into the database so some integrity constraints are enabled on the database, to check the accuracy of the database. Avoiding inconsistency: Consider there are two data storage sites of data and some changes are made in one site but those changes are not reflected on the other site for some reasons then it gives rise to data inconsistency. To avoid this data redundancy should be removed if data redundancy is checked it will also remove data inconsistency. Data Sharing: Suppose a same data is required by two databases then that data can be shared from a centralized database. Maintaining standards: As we know that DBMS is a centralized system so it can be standardized effectively. A company database can be standardized in Department Level, National Level, International Level, etc. Preventing unauthorized access: Lots of security can be enabled to prevent the unauthorized access of the data. Passwords and encryptions are enabled in database to provide better security to companyà ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒ ¢Ã¢â‚¬Å¾Ã‚ ¢s data. Backup and recovery of data: Data loss can happen at any moment due to number of reasons so it is very much important to create backup so that the data can be recovered if there is any accidental loss of data. Disadvantages of DBMS Complexity: The functionality of DBMS is a very complex process. The database designer, database administrator, developer and the end user should have a clear understanding of the DBMS working and functionality to make it work in a correct way. If they fail to do it DBMS will not work in a proper way. Size: As time passes the size or the volume of data increases which makes the size of database larger. Moving, copying and editing this large amount of data take a considerable time and system resource. Sometimes upgrading RAM or Disk Space becomes necessary to run the DBMS properly Performance: DBMS software tends to run slower than the typical File Processing System. Cost: Cost of implementing the DBMS is high. Sometimes the DBMS software for the specific environment is high, or the upgradeing of hardware to run it becomes costly or when converting from an older system to DBMS the process of conversion of the data costs a lot. Failure rate higher: As it is a centralized system I it fals every operation comes to a halt. Difference between File Management System and Database Management System File Management System Database Management System File management Systems are relatively small in size and volume Database Management Systems are comparatively larger in size. They are cheaper to implement Much costlier to implement It deals with few files It deals with a large number of files. The structure of this system is very simple The structure is very much complex in nature. There are many redundant data. Redundant data is very much reduced. Data inconsistency takes place in File Management System Data inconsistency is checked in the DBMS In File Management System data is isolated. In DBMS data can be shared. There is no security. It is secured. Very simple and primitive form of backup and recovery. Highly sophisticated and complex form of backup and recovery. Mainly single user. Most of the time its multiuser. Less preliminary design Vast preliminary design Purpose of DBMS Database Management System is very useful and is used in many sectors. Some the areas where it is used is given below. Railway: for making the railway enquiry and reservation system to work properly there is a needed of implementing DBMS as all the data has to be stored in a centralized location and the data is then used by every railway stations throughout the country. Banking: As now a days there are many branches of a bank there has to be a DBMS to track and record the transactions of every customers. This will have not been possible if File Management System were used. Schools/Colleges and Universities: To store the records of students like their name, roll number, address, contact number, marks obtained, etc a DBMS is used so that the database can be updated easily. Business and Offices: To store the companyà ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒ ¢Ã¢â‚¬Å¾Ã‚ ¢s sales, profit , etc and its record of employees a sophisticated DBMS is used. Instances and Schemas The database changes from time to time, the information which is stored in the database at a particular time is known as Instance. A Schema is a overall design of the database . QUESTIONS What do you mean by DBMS? A DBMS or Database Management System is a collection of data and programs which help us to access and modify those data. The collection of data is called database. The main purpose of DBMS is to efficiently store and control the database. How does DBMS differs from MS Excel? What are the differences between data and information? What is the difference between Conceptual Model and Physical Model? Conceptual Model: Conceptual Model helps to overview the organizational schema rather than the database schema. Physical Model: Physical Model is the database design which means that this model describes the data storage, data structure, etc. basically we get to know about the physical media of the data storage and the mode of access of that data from this model. What are the difference between Traditional File Management System and Database Management System? Traditional File Management System Database Management System File management Systems are relatively small in size and volume Database Management Systems are comparatively larger in size. They are cheaper to implement Much costlier to implement It deals with few files It deals with a large number of files. The structure of this system is very simple The structure is very much complex in nature. There are many redundant data. Redundant data is very much reduced. Data inconsistency takes place in File Management System Data inconsistency is checked in the DBMS In File Management System data is isolated. In DBMS data can be shared. There is no security. It is secured. Very simple and primitive form of backup and recovery. Highly sophisticated and complex form of backup and recovery. Mainly single user. Most of the time its multiuser. Less preliminary design Vast preliminary design What do you mean by Data Duplicity? Data Duplicity means repetition of the same data more than once in the same database. Data Duplicity causes lot of problems like: It is waste of time and money. It leads to loss of data integrity. It takes up additional storage and increases the size of the database which effects the search time. What do you mean by Data Dependency? In a File Processing System the specific physical format of file and record were hard coded on the application programs. So a change in database format required the codes to be updated. What is data isolation? Data Isolation means a data which is isolated form other databases that means when data can not be shared. It is not possible to share data in a File Processing System so the data remains in a isolated condition. Write down the necessary steps to secure a data into a database. What do you mean by redundant data? Data Redundancy means duplication of data. A same data may be present more than onces leading to data inconsistency. Write down the applications of Database Management System. There are many applications of Database Management System , some of them are explained below: Railway: for making the railway enquiry and reservation system to work properly there is a needed of implementing DBMS as all the data has to be stored in a centralized location and the data is then used by every railway stations throughout the country. Banking: As now a days there are many branches of a bank there has to be a DBMS to track and record the transactions of every customers. This will have not been possible if File Management System were used. Schools/Colleges and Universities: To store the records of students like their name, roll number, address, contact number, marks obtained, etc a DBMS is used so that the database can be updated easily. Business and Offices: To store the companyà ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒ ¢Ã¢â‚¬Å¾Ã‚ ¢s sales, profit , etc and its record of employees a sophisticated DBMS is used. Who are the End Users? How many types of End Users are there in DBMS? End User: The one who uses the database, it may be that he/she only views the database or it may be that he/she makes the data entries. Make queries, etc There may be different types of end user, for example : Sophisticated: these are the users who has a good knowledge in database and can make queries, with SQL manipulate data with DML (Data Manipulating Language) Specialized: who makes application programs that interacts with the database Native: only interacts with the database via some sophisticated programs

national Guard Essay -- essays research papers

What is The Pennsylvania Army National Guard? The Pennsylvania Army National Guard is a branch of the United States Army that is mostly used today for homeland security. Although the National Guard is not active like the Reserves or Active Duty Army, they still have the same requirements and same responsibility. More than 22,000 men and women make up the Pennsylvania National Guard and Air National Guard today. They reach from state quarters at Fort Indian Town Gap in Lebanon County to about 100 comunities in the commonwealth. Like all National Guard members they share the same responsibilities. For their federal mission, they are trained and equipped to join the active forces in the time of war or a national emergency. For their state mission, they respond to the orders of the governor, protecting the lives and property of people during man made and natural disasters. Their role extends further than floods, blizzards, and riots, everyday they work to clean up the enviorment, fighting to get rid of drugs and other illegal t hings on the streets, and they serve as role models to generations to come. With the National Guard today you can earn extra money for college, learn job skills that you can use out of the service, and feel better about yourself by serving your hometown and country. History of the National Guard The history of the Army National Guard began on December 13, 1636 when the Massachusetts Bay Colony organized three militia regiments to defend against the growing threat of the Pequot Indians. Patterned after the English Militia systems, all males between 16 and 60 Kessler 2 were obligated to own arms and take part in the defense of the community. The National Guard continues its historic mission of providing defense of the nation. The National Guard also fought many battles in the 20th century. The first war they were in during the 20th century was World War 1. From the streets of Harlem and other New York City neighborhoods came the African-American National... ...the advance into Iraq. Armed with the Multiple-Launch Rocket System, the Field Artillery men of this battalion provided accurate and devastating fire throughout the entire campaign. The rockets were so deadly; the Iraqi soldiers called them "steel rain." Today the Guard continues its vital peacekeeping effort in Southwest Asia. What it takes to be in the National Guard There are many requirements to be in the Army National Guard. The National Guard has physical, academic, and legal requirements that you must pass to join. You must first be in good physical shape and not have any major handicaps. The minimum age to join the National Guard is 17 years of age and a high school junior. If you are under the age of 18 you must have your parents consent. If you are not in high school you must have a high school diploma or GED. You must also score a high enough score on the ASVAB test. When you get your score you will be contacted by a recruiter to see what kind of job you want and can get by your score. You must also be a citizen of the United States and if you are an alien then you cannot get a job in the Army that requires a security clearance.

Cultural Competency Assessment Essay

The Long Island Adolescent and Family Services or LIAFS is a social service organization that assists the young population in their needs and concerns. LIAFS first started as a support group for young people who were victims of crime and a haven for children who do not have anywhere to go. LIAFS does not operate for the purpose of generating profit. The organization is headstrong in providing support and assistance to adolescents or families who are experiencing difficulties and challenges in whatever aspect of their lives. (LIAFS, 2008) LIAFS obtains its resources to accomplish its goals and objectives from donations and contributions. People who want to help LIAFS further their aims are able to send their monetary assistance to the LIAFS’ office. As of now, the organization is publishing a wish list that contains what they need, such as computers, sports equipments, vehicles, and gifts that the organization will be able to give out to children during their birthdays. (LIAFS, 2008b) The organization is also in need of employees that are willing to work for the said cause, such as cooks, therapists, drivers, counselors, and psychologists. (LIAFS, 2008c) The involvement of the community or the neighborhood is much needed in running programs of the LIAFS. This is highly recommended, especially for non-profit organizations, if LIAFS wishes to sustain resources and take one step higher than what the organization has been attaining or has attained. This concept builds on cultural competency. Cultural competency means that a non-profit organization is able to become culturally aware and through it gain help from diverse groups in society in order to assist the organization is realizing its goals and objectives. (Alliance for Non-Profit Management, 2004) In general, cultural competency assists the organization in providing much-needed quality services to the youth and their families. The need for cultural competency requires LIAFS to employ the help of the community or the neighborhood in understanding a multitude of cultures in order to provide suitable and comprehensive services to cover for the needs and concerns of youth and families. Employing the help of community members requires the need to train them in cultural issues so they too can help in providing needs and concerns that the LIAFS originally provides the youth and their families. Involvement of the community or neighborhood should be on a regular basis because its members need to understand that the welfare of the youth and their families influence the conditions of the community and the neighborhood. Aside from cultural awareness to deal with people belonging to diverse cultural backgrounds and other groups or organizations that operate on a different cultural environment or situation, people from the community or the neighborhood should be able to be informed about the situations that the youth and their families are experiencing. It is important for them to discern that the adolescent population and their families who are experiencing difficulties and challenges need the help of other people for them to be able to improve their situation and way of life. Part of the contribution of communities and neighborhoods include being observant or vigilant about their surroundings. It is important to supervise events or situations in the neighborhood in order to perceive potential risks or harmful environments that might endanger children and their families. Keeping a watchful eye prevents difficult situations such as child abuse, for instance. It is also the role of the community and the neighborhood to protect the rights of the citizens. Once an incident happens, it is their responsibility to act against crime and other events that lead to difficulties and challenges. Another role that the community or neighborhood should play is to be involved in the fund-raising process most especially because LIAFS is a non-profit organization. Members of the community or neighborhood should be able to contribute to LIAFS’ programs and activities with whatever assistance they can extend to the organization, the children, and their families. Community volunteerism is something that should be practiced by members of the community or neighborhood. References Alliance for Non-Profit Management. (2004). â€Å"Cultural Competency Initiative. † Retrieved August 30, 2008, from Alliance for Non-Profit Management. Website: http://www. allianceonline. org/cci. ipage LIAFS. (2008). â€Å"About LIAFS. † Retrieved August 30, 2008, from LIAFS. Website: http://www. liafs. org/about. html LIAFS. (2008b). â€Å"Support and Donations. † Retrieved August 30, 2008, from LIAFS. Website: http://www. liafs. org/donation. html LIAFS. (2008c). â€Å"Employment Opportunities. † Retrieved August 30, 2008, from LIAFS. Website: http://www. liafs. org/employment. html

Informative Essay Sample on Theories of Evolution

Informative Essay Sample on Theories of Evolution The creation-evolution controversy is everlasting verbal battle between the advocates of two theories. During those disputes many brilliant ideas appeared, and even more ideas were demolished. Theories of evolution and creation involved best scientists: theologists, biologists, medics, sociologists, geologists, paleontologists, nuclear physicists and cosmologists. Those theories even acquired a â€Å"foster child†, so called theistic evolution theory. In this essay I will discuss the origins and consequences of the creation-evolution controversy, emphasize main points of three theories and sum everything up with my personal opinion. The theory of evolution (also called Metaphysical naturalism) probably originated in early Greek philosophy. In their definition of nature, Greeks distinguished natural from artificial. During the Renaissance, which reintroduced numerous treatises by Greek and Roman natural philosophers, and many of the ideas and concepts of naturalism were conceived. In this period, metaphysical naturalism finally acquired a distinct name, materialism. However, in the 20th century advances in physics as well as philosophy made the whole idea of materialism untenable. Matter was found to be a form of energy so reality was obviously not so â€Å"material† as it used to be thought of. As a final note to the history of metaphysical naturalism, Marxism, a variety of politicized naturalism, appeared. However, today most advocates of metaphysical naturalism reject both extremes and embrace the more moderate political ideals. While the adherents of evolutionary theory were disputing whose ideas is mo re â€Å"absolutely right† new theory the theory of theistic evolution appeared. Theistic evolution (or evolutionary creationism) is the general opinion that the theory of creation is compatible understanding about biological evolution. Theistic evolution supporters can be seen as the group, who try to avoid conflicts between religion and science, they are sure that teachings about creation and scientific theories of evolution need not be contradictory. This term was first used by Eugenie Scott to refer to the beliefs about creation and evolution holding the theological view that God creates through evolution. This view is accepted by major Christian churches, including Roman Catholicism and some Protestant denominations. The major criticism of theistic evolution by non-theistic evolutionists focuses on its essential belief in a supernatural creator, evolutionists state that theistic evolution is simply a belief in a God of the gaps, where anything that cannot currently be explained by science is attributed to God. Intelligent design (also known as creationism) is the assertion that certain features of the universe and of living things are best explained by an intelligent cause, not an undirected process such as natural selection. The term intelligent design came into use after the U.S. Supreme Court ruled in the 1987 case of Edwards v. Aguillard who claimed that the teaching of creation science alongside evolution was a violation of the Establishment Clause, which prohibits state aid to religion. The term irreducible complexity was introduced by Michael Behe, who defines it as a single system which is composed of several well-matched interacting parts that contribute to the basic function, wherein the removal of any one of the parts causes the system to effectively cease functioning.For that time their claims were declared to be unsupported, but in a long run it turns out that sympathizers of creation idea have the strongest arguments. Fine Tuning argument, for example, claims that the fundame ntal constants of physics and laws of nature appear so finely-tuned to permit life that only a supernatural engineer can explain it. Attentive reader should have already noticed that I am the supporter of creation theory. In my personal view neither the naturalism theory nor the theory of theistic evolution can not (and never will) explain the existence of morality, emotions, conscience, love. Only God who has all aforementioned features could give them to us. In a conclusion I would like to mention the defiance that Dr. Kent Hovind bid to all advocates of evolution theory in far 1990. I have a standing offer of $250,000 to anyone who can give any empirical evidence (scientific proof) for evolution. My $250,000 offer demonstrates that the hypothesis of evolution is nothing more than a religious belief.No one still managed to get this reward. Maybe YOU want to be the first?;)

Avoid Career Regret with These 6 Tips

Avoid Career Regret with These 6 Tips Getting a job is hard enough, but if you’re playing the long game for career fulfillment and success, it’s never a good idea to rest too long on your laurels. Rather than getting lazy and complacent, why not stay hungry and strategic, and keep your eyes on that ultimate prize, whatever yours may be. Here are 6 things you should always keep in mind if you want to look back and not have any career regrets. Click for more.Don’t always put money firstObviously, it’s important to make enough to support yourself and whomever else you need to care for. But constantly making moves in order to maximize what you make? That can lead you into all sorts of unsatisfying situations.Once you reach a certain threshold of financial comfort, ask yourself with each potential move: is this going to make me happier or just more rich? Focus on work that keeps you interested, challenged, and smiling on your way to work. Rather than the drudgery or soul-selling for the fancy pay-o ut. It’s also a great way to avoid burning out.Push your own boundariesEvery so often, push yourself out of your own comfort zone. Try taking an opportunity you might ordinarily say no to, or learning a skill you didn’t think you’d ever need. The broader your interests and skills, the more you’ll get out of your work life.Trust your gutIf you sense impending shake-ups or lay-offs or feel you’re on a sinking ship, be smart and start looking before disaster strikes. If a position doesn’t smell right for some reason? Take your time and look elsewhere. Learning to hone (and trust!) your instincts can be an invaluable skill that will help steer you straight for your entire career.Keep ‘em sweetIf ever you have to leave a hell job, or quit on a demon boss, resist the temptation to burn bridges. Take the moral high road, keep it classy, walk out with your head held high having done everything you could to stay respectful. You never know when you might run into former colleagues or supervisors again down the line. Gain a reputation for professionalism, not pique.Stay sharpKeep a constant eye on the trends in your field. That means watching out for new systems and software, participating in additional training, keeping on top of new qualifications you can acquire. Staying devoted to learning will nurture you in multiple ways, but will also keep you fiercely marketable.Shoot for the moonYou know, in nice and steady, measured, incremental shots. Do have a big dream and do pursue it. Just try to do so as smartly and well-preparedly as you can. Put your big dream on a hidden post-it somewhere and keep that in mind as you go through every humdrum workday. Eye on the prize.

A Supermarket in California Essay Example | Topics and Well Written Essays - 750 words

A Supermarket in California - Essay Example This essay examines Ginsberg’s ‘A Supermarket in California’ in terms of figurative language and poetic technique. One of the most overarching considerations is that the poem is partially meant to be a tribute to Walt Whitman and was released on the centennial of Whitman’s ‘Leaves of Grass.’ This is clearly reflected in the poem as Ginsberg makes frequent reference to Walt Whitman within the poem’s very narrative structure. Indeed, Ginsberg wistfully addresses Whitman in a number of ways. Ginsberg states, â€Å"What thoughts I have of you tonight, Walt Whitman, for I walked down the side streets under the trees with a headache† (Ginsberg, 1-2). The poem also contains long-lines that are slightly unique. Partly these long-lines can be attributed to a further tribute to Whitman’s own tendency towards incorporating this form in his poems. Notably, poet Garcia-Lorca is also referenced in this work. In terms of poetic form the p oem is highly unique in that it does not conform to traditional types of stanza or rhyme scheme. What can be termed the first stanza extends for twelve lines; three more stanzas of varying line length follow this stanza. There is no discernable rhyme scheme in the poem, with Ginsberg refraining from even implementing a rhyming couplet. Additionally, these elements that eschew traditional poetic form are clearly in-line with the Beat Movement’s embrace of alternative modes of expression. Additionally, the poem’s narrative -- as embracing American counter-cultural elements -- is perhaps best articulated by an irregular form. The narrative as embracing counter-cultural elements is indeed a major consideration within this work. Ginsberg writes, â€Å"I saw you, Walt Whitman, childless, lonely old grubber, poking among the meats in the refrigerator and eyeing the grocery boys† (Ginsberg, 11-12). Here there is the obvious allusion to homosexuality through the eyeing o f grocery boys. While poetic form constitutes a major element within this specific work, the narrative has perhaps gained the most critical attention. As noted the work is partly a tribute to Walt Whitman. Further analysis reveals a number of notable elements. During Whitman’s there is the recognition of American society as more in direct contact with natural elements. The setting of the poem’s narrative within the supermarket then is perhaps a means of ironically commenting on the nature of industrialized world as far removed from the direct process of hunting and growing their own subsistence. This interpretation is heightened by Ginsberg’s subtly comedic line, â€Å"I heard you asking questions of each: Who killed the pork chops? What price bananas? Are you my Angel?† (Ginsberg, 13-14). Here one recognizes the comedic potential of placing Whitman in a modern day supermarket. While the poem embraces irregular poetic form and counter-cultural elements, t here is also the recognition that Ginsberg expertly interweaves a number of profound themes. One such consideration is the meditation on the present day cultural milieu. Ginsberg writes, â€Å"Will we stroll dreaming of the lost America of love past blue automobiles in driveways, home to our silent cottage?† (Ginsberg, 26-27). Just as Whitman articulated a profound 19th century vision of America, Ginsberg here is working to capture the essence of time. In addition to

We are writing a news story or a profile of someone interesting Essay

We are writing a news story or a profile of someone interesting. Perhaps you can interview someone in Saudi Arabia and write a story about them - Essay Example Being an assistant clinical professor of psychiatry at a reputed Jeddah hospital, Mr. Karim described how his life had changed since he stepped into the field of psychiatry. I started by asking him what the general responsibilities of his profession were. He replied that his main duty was to provide high quality psychiatric services, which included medication and therapeutic advice and sessions, to patients who came to him with a myriad of psychiatric problems. He worked both as a therapist and a medication advisor for most of the patients; while for some, he was only the medical advisor. He described that a typical shift of a psychiatrist included sessions with psychiatric patients. Each session was 30 minutes long, and he saw around 10 patients in a day. Continuing the interview, Mr. Karim explained that the biggest challenge he faced while working with a hospital was that, in contrast to private practice, he was not able to manage his working hours, which were set by the hospital authority. So, he did not have control over his time and schedule, which he missed when he was working as a solo practitioner in the past. He had to give explanations and put leave applications if he wanted to go on a leave. Also, the hospital demanded that he should be available to patients on phone all the time, 24/7, which was something that he was well managing as a solo practitioner. The hospital would also call him on weekends, on and off. He said that while working with the patients with difficult psychiatric conditions, it was sometimes very stressful for him to deal with stressors. Forgetting one’s own worries and dealing with others’ was what was required of him, which he was doing very well, but at times, he would become stressed out. He described how some patients gave him tough time. Some patients would attack him verbally to let go of their frustrations and anxiety, and it would become very hard for him to keep himself calm. However, he