Partitioning Methods to Improve Obsolescence Forecasting

Partitioning Methods to Improve Obsolescence Forecasting Amol Kulkarni Abstract Clustering is an unsupervised classification of observations or data items into groups or clusters. The problem of clustering has been addressed by many researchers in various disciplines, which serves to reflect its usefulness as one of the steps in exploratory data analysis. This paper presents an overview of partitioning methods, with a goal of providing useful advice and references to identifying the optimal number of cluster and provide a basic introduction to cluster validation techniques. The aim of clustering methods carried out in this paper is to present useful information which would aid in forecasting obsolescence. INRODUCTION There have been more inventions recorded in the past thirty years than all the rest of recorded humanity, and this pace hastens every month. As a result, the product life cycle has been decreasing rapidly, and the life cycle of products no longer fit together with the life cycle of their components. This issue is termed as obsolescence, wherein a component can no longer be obtained from its original manufacturer. Obsolescence can be broadly categorized into Planned and Unplanned obsolescence. Planned obsolescence can be considered as a business strategy, in which the obsolescence of a product is built into it from its conception. As Philip Kotler termed it Much so-called planned obsolescence is the working of the competitive and technological forces in a free society-forces that lead to ever-improving goods and services. On the other hand, unplanned obsolescence causes more harm to a burgeoning industry than good. This issue is more prevalent in the electronics industry; the procurem ent life-cycles for electronic components are significantly shorter than the manufacturing and support life-cycle. Therefore, it is highly important to implement and operate an active management of obsolescence to mitigate and avoid extreme costs [1]. One such product that has been plagued by threat of obsolescence is the digital camera. Ever-since the invention of smartphones there has been a huge dip in the digital camera sales, as can be seen from Figure 1. The decreasing price, the exponential rate at which the pixels and the resolution of the smart-phones improved can be termed as few of the factors that cannibalized the digital camera market. Figure 1 Worldwide Sales of Digital Cameras (2011-2016) [2] and Worldwide sale of cellphones on the right (2007-2016) [3] CLUSTERING Humans naturally use clustering to understand the world around them. The ability to group sets of objects based on similarities are fundamental to learning. Researchers have sought to capture these natural learning methods mathematically and this has birthed the clustering research. To help us solve problems at-least approximately as our brain, mathematically precise notation of clustering is important [4]. Clustering is a useful technique to explore natural groupings within multivariate data for a structure of natural groupings, also for feature extraction and summarizing. Clustering is also useful in identifying outliers, forming hypotheses concerning relationships. Clustering can be thought of as partitioning a given space into K groups i.e., à °Ã‚ Ã¢â‚¬ËœÃ¢â‚¬Å": à °Ã‚ Ã¢â‚¬ËœÃ¢â‚¬ ¹ à ¢Ã¢â‚¬  Ã¢â‚¬â„¢ {1, à ¢Ã¢â€š ¬Ã‚ ¦, K}. One method of carrying out this partitioning is to optimize some internal clustering criteria such as the distance between each observation within a c luster etc. While clustering plays an important role in data analysis and serves as a preprocessing step for a multitude of learning task, our primary interest lies in the ability of clusters to gain more information from the data to improve prediction accuracy. As clustering, can be thought of separating classes, it should help in classification task. The aim of clustering is to find useful groups of objects, usefulness being defined by the goals of the data analysis. Most clustering algorithms require us to know the number of clusters beforehand. However, there is no intuitive way of identifying the optimal number of clusters. Identifying optimal clustering is dependent on the methods used for measuring similarities, and the parameters used for partitioning, in general identifying the optimal number of clusters. Determining number of clusters is often an ad hoc decision based on prior knowledge, assumptions, and practical experience is very subjective. This paper performs k-means and k-medoids clustering to gain information from the data structure that could play an important role in predicting obsolescence. It also tries to address the issue of assessing cluster tendency, which is a first and foremost step while carrying out unsupervised machine learning process. Optimization of internal and external clustering criteria will be carried out to identify the optimal number of cluster. Cluster Validation will be carried out to identify the most suitable clustering algorithm. DATA CLEANING Missing value in a dataset is a common occurrence in real world problems. It is important to know how to handle missing data to reduce bias and to produce powerful models. Sometimes ignoring the missing data, biases the answers and potentially leads to incorrect conclusion. Rubin in [7] differentiated between three types of missing values in the dataset: Missing completely at random (MCAR): when cases with missing values can be thought of as a random sample of all the cases; MCAR occurs rarely in practice. Missing at random (MAR): when conditioned on all the data we have, any remaining missing value is completely random; that is, it does not depend on some missing variables. So, missing values can be modelled using the observed data. Then, we can use specialized missing data analysis methods on the available data to correct for the effects of missing values. Missing not at random (MNAR): when data is neither MCAR nor MAR. This is difficult to handle because it will require strong assumptions about the patterns of missing data. While in practice the use of complete case methods which drops the observations containing missing values is quite common, this method has the disadvantage that it is inefficient and potentially leads to bias. Initial approach was to visually explore each individual variable with the help of VIM. However, upon learning the limitations of filling in missing values through exploratory data analysis, this approach was abandoned in favor of multiple imputations. Joint Modelling (JM) and Fully Conditional Specification (FCS) are the two emerging general methods in imputing multivariate data. If multivariate distribution of the missing data is a reasonable assumption, then Joint Modelling which imputes data based on Markov Chain Monte Carlo techniques would be the best method. FCS specifies the multivariate imputation model on a variable-by-variable basis by a set of conditional densities, one for each incomplete variable. Starting from an initial imputation, FCS draws imputations by iterating over the conditional densities. A low number of iterations is often sufficient. FCS is attractive as an alternative to JM in cases where no suitable multivariate distribution can be found [8]. The Multiple imputations approach involves filling in missing values multiple times, creating multiple complete datasets. Because multiple imputations involve creating multiple predictions for each missing value, the analysis of data imputed multiple times take into account the uncertainty in the imputations and yield accurate standard errors. Multiple imputation techniques have been utilized to impute missing values in the dataset, primarily because it preserves the relation in the data and it also preserves uncertainty about these relations. This method is by no means perfect, it has its own complexities. The only complexity was having variables of different types (binary, unordered and continuous), thereby making the application of models, which assumed multivariate normal distribution- theoretically inappropriate. There are several complexities that surface listed in [8]. In order to address this issue It is convenient to specify imputation model separately for each column in th e data. This is called as chained equations wherein the specification occurs at a variable level, which is well understood by the user. The first task is to identify the variables to be included in the imputation process. This generally includes all the variables that will be used in the subsequent analysis irrespective of the presence of missing data, as well as variables that may be predictive of the missing data. There are three specific issues that often come up when selecting variables: (1) creating an imputation model that is more general than the analysis model, (2) imputing variables at the item level vs. the summary level, and (3) imputing variables that reflect raw scores vs. standardized scores. To help make a decision on these aspects, the distribution of the variables may help guide the decision. For example, if the raw scores of a continuous measure are more normally distributed than the corresponding standardized scores then using the raw scores in the imputation model, will likely better meet the assumptions of the linear regressions being used in the imputation process. The following image shows the missing values in the data-frame containing the information regarding digital camera. Figure 2 Missing Variables We can see that Effective Pixels has missing values for all its observations. After cross verifying it with the source website, the web scrapper was rewriting to correctly capture this variable from the website. The date variable was converted from a numeric to a date and this enabled the identification of errors in the observation for USB in the dataset. Two cameras that were released in 1994 1995 were shown to have USB 2.0, after searching online, it was found out that USB 2.0 was released in the year 2005 and USB 1.0 was released in the year 1996. As, most of the cameras before 1997 used PC-serial port a new level was introduced to the USB variable to indicate this. DATA DESCRIPTION The dataset containing the specification of the digital cameras was acquired using rvest -package [5] in R from the url provided in [6]. The structure of the data set is as shown in Appendix A. The data-frame contains 2199 observation and 55 variables. Appendix B contains the descriptive statistics of the quantitative variables in the data-frame. Figure 4 The Distribution of Body-Type in the dataset Observation: Most of the compact, Large SLR and ultracompact cameras are discontinued. Figure 5 Plot showing the status of Digital Cameras from 1994-2017 Observation: Most of the cameras released before 2007 have been discontinued however, we can see that few cameras announced between the period of 1996-2006 are still in production. Fewer new cameras have been announced after the year 2012, this can be evidenced due to the decreasing number of camera sales presented in Figure 5. Figure 6 Distribution of different Cameras (1994-2017) Observation: Between the period of 1996 2012 the digital camera market was dominated by the compact cameras. After 2012, fewer new compact cameras have been announced or are still in production. Same can be said about the fate of ultracompact cameras. In the year 2017, only SLR style mirrorless cameras have been announced, signaling the death of point and shoot cameras. Figure 7 Plot showing the Change in the Total Resolution and Effective Pixels of Digital Camera over the Years Observation: Total resolution has seen an improvement over the years. The presence of outliers can be seen in the top-left corner of the plot. Although the effective pixel is around 10, the total resolution is far higher than any of the cameras announced between the period 1996-2001. These could be the cameras that are still in production as evidenced from Figure 7. ASSESSING CLUSTER TENDENCY A primary issue with unsupervised machine learning is the fact if carried out blindly, clustering methods will divide the data into clusters, because that is what they are supposed to do. Therefore, before choosing a clustering approach, it is important to decide whether the dataset contains meaningful clusters. If the data does contain meaningful clusters, then the number of clusters is also an issue that needs to be looked at. This process is called assessing clustering tendency (feasibility of cluster analysis). To carry out a feasibility study of cluster analysis Hopkins statistic will be used to assess the clustering tendency of the dataset. Hopkins statistic assess the clustering tendency based on the probability that a given data follows a uniform distribution (tests for spatial randomness). If the value of the statistic is close to zero this implies that the data does not follow uniform distribution and thus we can reject the null hypothesis. Hopkins statistic is calculated using the following formula: Where xi is the distance between two neighboring points in a given, dataset and yi represents the distance between two neighboring points of a simulated dataset following uniform distribution. If the value of H is 0.5, this implies that and are close to one another and thus the given data follows a uniform distribution. The next step in the unsupervised learning method is to identify the optimal number of clusters. The Hopkins statistic for the digital camera dataset was found to be 0.00715041. Since Hopkins statistic was quite low, we can conclude that the dataset is highly clusterable. A visual assessment of the clustering tendency was also carried out and the result can be seen in Figure 8. Figure 8 Dissimilarity Matrix of the dataset DETERMINING OPTIMAL NUMBER OF CLUSTERS One simple solution to identify the optimal number of cluster is to perform hierarchical clustering and determine the number of clusters based on the dendogram generated. However, we will utilize the following methods to identify the optimal number of clusters: An optimization criterion such as within sum of squares or Average Silhouette width Comparing evidence against null hypothesis. (Gap Statistic) SUM OF SQUARES The basic idea behind partitioning methods like k-means clustering algorithms, is to define clusters such that the total within cluster sum of squares is minimized. Where Ck is the kth cluster and W(Ck) is the variation within the cluster. Our aim is to minimize the total within cluster sum of squares as it measures the compactness of the clusters. In this approach, we generally perform clustering method, by varying the number of clusters (k). For each k we compute the total within sum of squares. We then plot the total within sum of squares against the k-value, the location of bend or knee in the plot is considered as an appropriate value of the cluster. AVERAGE SILHOUETTE WIDTH Average silhouette is a measure of the quality of clustering, in that it determines the how well an object lies within its cluster. The metric can range from -1 to 1, where higher values are better. Average silhouette method computes the average silhouette of observations for different number of clusters. The optimal number of clusters is the one that maximizes the average silhouette over a range of possible values for different number of clusters [9]. Average silhouette functions similar to within sum of squares method. We carry out the clustering algorithm by varying the number of clusters, then we calculate average silhouette of observation for each cluster. We then plot the average silhouette against different number of clusters. The location with the highest value of average silhouette width is considered as the optimum number of cluster. GAP STATISTIC This method compares the total within sum of squares for different number of cluster with their expected values while assuming that the data follows a distribution with no obvious clustering. The reference dataset is generated using Monte Carlo simulations of the sampling process. For each variable (xi) in the dataset we compute its range [min(xi), max(xj)] and generate n values uniformly from the range min to max. The total within cluster variation for both the observed data and the reference data is computed for different number of clusters. The gap statistic for a given number of cluster is defined as follows: denotes the expectation under a sample of size n from the reference distribution. is defined via bootstrapping and computing the average . The gap statistic measures the deviation of the observed Wk value from its expected value under the null hypothesis. The estimate of the optimal number of clusters will be a value that maximizes Gapn(k). This implies that the clustering structure is far away from the uniform distribution of points. The standard deviation (sdk) of is also computed in order to define the standard error sk as follows: Finally, we choose the smallest value of the number of cluster such that the gap statistic is within one standard deviation of the gap at k+1 Gap(k)à ¢Ã¢â‚¬ °Ã‚ ¥Gap(k+1) sk+1 The above method and its explanation are borrowed from [10]. DATA PRE-PROCESSING The issue with K-means clustering is that it cannot handle categorical variables. As the K-means algorithm defines a cost function that computes Euclidean distance between two numeric values. However, it is not possible to define such distance between categorical values. Hence, the need to treat categorical data as numeric. While it is not improper to deal with variables in this manner, however categorical variables lose their meaning once they are treated as numeric. To be able to perform clustering efficiently, Gower distance will be used for clustering. The concept of Gower distance is that for each variable a distance metric that works well for that particular type of variable is used. It is scaled between 0 and 1 and then a linear combination of weights is calculated to create the final distance matrix. PARTITIONING METHODS K-MEANS K-means clustering is the simplest and the most commonly used partitioning method for splitting a dataset into a set of k clusters. In this method, we first choose K initial centroids. Each point is then assigned to the closest centroid, and each collection of points is assigned to a centroid in the cluster. The centroid of each cluster is updated based on the additional points assigned to the cluster. We repeat his until the centroids find a steady state. Figure 9 Plot Showing total sum of square and Average Silhouette width for different number of clusters We can see from Figure 9, that the optimal number of clusters suggested by the optimization criteria is 3 clusters using WSS method and 2 clusters using Average Silhouette width method. Considering the dependent variable is factor with two levels, having two clusters does make sense. The disadvantage of optimization criterion to identify the optimal clusters is that, it is sometimes ambiguous. A more sophisticated method is the gap statistic method. Figure 10 Gap Statistic for different number of clusters From Figure 10, we can see that the Gap statistic is high for 2 clusters. Hence, we carry out k-means clustering with 2 clusters on a majority basis. Figure 11 Visualizing K-means Clustering Method The data separates into two relatively distinct clusters, with the red category in the left region, while the region on the right contains the blue category. There is a limited overlap at the interface between the classes. To visualize K-means it is necessary to bring the number of dimensions down to two. The graph produced by fviz_cluster: Factoextra Ver: 1.0 [11] is not a selection of any two dimensions. The plot shows the projection of the entire data onto the first two principle components. These are the dimensions which show the most variation in the data. The 52.8% indicates that the first principle component accounts for 52.8% variation in the data, whereas the second principle component accounts for 23.9% variation in the data. Together both the dimensions account for 76.7% of the variation. The polygon in red and blue represent the cluster means. PARTITIONING AROUND MEDOIDS K means clustering is highly sensitive to outliers, this would affect the assignment of observations to their respective clusters. Partitioning around medoids also known as K-medoids clustering are much more robust compared to k-means. K-medoids is based on the search of medoids among the observation of the dataset. These medoids represent the structure of the data. Much like K-means, after finding the medoids for each of the K- clusters, each observation is assigned to the nearest medoid. The aim is to find K-medoids such that it minimizes the sum of dissimilarities of the observations within the cluster. Figure 12 Plot Showing total sum of square and Average Silhouette width for different number of clusters We can see from Figure 12, that the optimal number of clusters suggested by the optimization criteria is 3 clusters using WSS method and 2 clusters using Average Silhouette width method. Considering the dependent variable is factor with two levels, having two clusters does make sense. The disadvantage of optimization criterion to identify the optimal clusters is that, it is sometimes ambiguous. A more sophisticated method is the gap statistic method. Figure 13 Gap Statistic for different number of clusters From Figure 13, we can see that the Gap statistic is high for 2 clusters. Hence, we carry out partitioning around medoids clustering with 2 clusters on a majority basis. Figure 14 Plot visualizing PAM clustering method The data separates into two relatively distinct clusters, with the red category in the lower region, while the upper region contains the blue category. There is a limited overlap at the interface between the classes. fviz_cluster: Factoextra Ver: 1.0 [11] transforms the initial set of variables into a new set of variables through principal component analysis. This dimensionality reduction algorithm operates on the 72 variables and outputs the two new variables that represent the projection of the original dataset. CLUSTER VALIDATION The next step in cluster analysis is to find the goodness of fit and to avoid finding patterns in noise and to compare clustering algorithms, cluster validation is carried out. The following cluster validation measures to compare K-means and PAM clustering will be used: Connectivity: Indicates the extent to which the observations are placed in the same cluster as their nearest neighbors in the data space. It has a value ranging from 0 to à ¢Ã‹â€ Ã… ¾ and should be minimized Dunn: It is the ratio of shortest distance between two clusters to the largest intra-cluster distance. It has a value ranging from 0 to à ¢Ã‹â€ Ã… ¾ and should be maximized. Average Silhouette width The results of internal validation measures are presented in the table below. K-means for two cluster has performed better for each statistic. Figure 15 Plot Comparing Connectivity and Dunn Index for K-means and PAM for different number of clusters      Ã‚   Figure 16 Plot Comparing Average Silhouette width of K-means and PAM Clustering Algorithm Validation Measures Number of Clusters 2 3 4 5 6 kmeans Connectivity 139.9575 292.5563 406.5429 514.3913 605.5373 Dunn 0.0661 0.0246 0.0223 0.0244 0.0291 Silhouette 0.4369 0.3174 0.2814 0.2679 0.2447 pam Connectivity 156.1004 333.754 474.4298 520.3913 635.3687 Dunn 0.0275 0.0397 0.022 0.028 0.0246 Silhouette 0.4271 0.3035 0.2757 0.2661 0.2325 Table 1 Presenting the values of different validation measures for K-means and PAM Validation Measures Score Method Clusters Connectivity 139.9575 kmeans 2 Dunn 0.0661 kmeans 2 Silhouette 0.4369 kmeans 2 Table 2 Optimal Scores for the Validation Measures CONCLUSION In this research work, partitioning methods like K-means and Partitioning around medoids were developed. The performances of these two approaches have been observed on the basis of their Connectivity, Dunn index and Average Silhouette width. The results indicate that K-means clustering algorithm with K = 2 performs better than partitioning around medoids with two clusters. The findings of this paper will be very useful to predict obsolescence with higher accuracy. FUTURE WORK Advanced clustering algorithms such as Model based clustering and Density based clustering can be carried out to find the multivariate data structure as most of the variables are categorical. [1] Bjoern Bartels, Ulrich Ermel, Peter Sandborn and Michael G. Pecht (2012). Strategies to the Prediction, Mitigation and Management of Product Obsolescence. [2] Source Figure 1: https://www.statista.com/statistics/269927/sales-of-analog-and-digital-cameras-worldwide-since-2002/ [3] Source, Figure 1: https://www.statista.com/statistics/263437/global-smartphone-sales-to-end-users-since-2007/ [4] S. Still, and W. Bialek, How many Clusters? An Information Theoretic Perspective, Neural Computation, 2004. [5] Wickham, Hadley, rvest: Easily Harvest (Scrape) Web Pages. https://cran.r-project.org/web/packages/rvest/rvest.pdf, Ver. 0.3.2 [6] https://www.dpreview.com [7] Rubin, D.B., Inference and missing data. Biometrika, 1976. [8] Multivariate Imputation by Chained Equations Stef van Buuren, Karin Groothuis . [9] Learning the k in k-means Greg Hamerly, Charles Elkan [10] Robert Tibshirani, Guenther Walther and Trevor Hast

Assess the usefulness of social action theories in the study of society Essay

Social action theories are known as micro theories which take a bottom-up approach to studying society; they look at how individuals within society interact with each other. There are many forms of social action theories, the main ones being symbolic interactionism, phenomenology and ethnomethodology. They are all based on the work of Max Weber, a sociologist, who acknowledged that structural factors can shape our behaviour but individuals do have reasons for their actions. He used this to explain why people behave in the way in which they do within society. Weber saw four types of actions which are commonly committed within society; rational, this includes logical plans which are used to achieve goals, traditional-customary behaviour, this is behaviour which is traditional and has always been done; he also saw affectual actions, this includes an emotion associated with an action and value-rational actions, this is behaviour which is seen as logical by an individual. Weber’s discovery of these actions can therefore be seen as useful in the study of society. Weber discovered these actions by using his concept of verstehan, a deeper understanding. However, some sociologists have criticised him as they argue that verstehan cannot be accomplished as it is not possible to see thing in the way that others see them, leaving sociologists to question whether Weber’s social action theory is useful in the study of society. Social action theories have also been referred to as interactionism as they aim to explain day-to-day interactions between individuals within society. G. H Mead came up with the idea of interactionism and argued that the self is ‘a social construction arising out of social experience’. This is because, according to Mead, social situations are what influence the way in we act and behave. He claims that we develop a sense of self as a child and this allows us to see ourselves in the way in which other people see us; we act and behave in certain ways depending on the circumstances which we are in. Mead also claimed that we have a number of different selves which we turn into when we are in certain situations; i. e. we may have one self for the work place and another self for home life. Mead concluded that society is like a stage, in which we are all ‘actors’. Mead’s theory if interactionism is useful in the study of society as it explains why people behave in different ways in certain situations. Mead argues that the social context of a situation is what influences our behaviour, humans use symbols, in the form of language and facial expressions, to communicate, he also argued that humans and animals differ as reasons behind humans’ actions are thought through and not instinctive, unlike those of animals’. However, it has been argued that not all action is meaningful, as Weber’s category of traditional action suggests that much action is performed unconsciously and may have little meaning. Therefore, mead’s idea of interactionism cannot be seen as an appropriate theory to use when studying society. Blumer, a sociologist, who elaborated on Mead’s concept of the self – ‘I’ and ‘me’ – stated that there were three principles about actions and behaviours within social situations. He argued that our actions are the result of situations and events and they have reasons. The reasons behind our actions are negotiable and changeable, so they’re not fixed. Our interpretation of a situation is what gives it meaning. Blumer’s three principles can therefore be used in the study of society. However, it has been argued that his principles cannot explain the consistent patterns which we see in people’s behaviours. This therefore leaves many sociologists to question whether Blumer’s principles can be used to study society. Labelling theory has also been used to apply the interactionist theory to society; the theory, like Mead, emphasises the importance of symbols and situations in which they are used. The main interactionist concepts are the definition of the situation – if we believe in something then it could affect the way in which we behave. The looking glass –self – this was created by Cooley who argues that we see ourselves in a way in which we think others see us. These concepts have been useful in explaining why people act in certain ways in certain situations; therefore, the labelling theory is effective in the study of society. Overall, in conclusion, there are many different social action theories which can be used in the study of society, however, not all of them can be applied to all individuals.

Network Server Administration

Course number CIS 332, Network Server Administration, lists as its main topics: installing and configuring servers, network protocols, resource and end user management, security, Active Directory, and the variety of server roles which can be implemented. My experience and certification as a Microsoft Certified System Administrator (MCSA) as well as a Microsoft Certified System Engineer (MCSE) demonstrates that I have a thorough grounding in both the theory and practice of the topics covered in this course and should receive credit for it. Installing and configuring servers was the subject of Installing, Configuring and Administering Microsoft Windows 2000 Server, which I took in 2001 in preparation for my initial Microsoft Certified Professional certification. This exam covered such topics as installing Microsoft Windows 2000 Server using both an attended installation and an unattended installation; server upgrades from Windows NT (the previous version) and troubleshooting and repairing failed installations. This exam also covered installing and configuring hardware devices and user management. Network protocols were discussed during the training for the exam Implementing and Administering a Microsoft Windows 2000 Network Infrastructure, which I also took in 2001. This exam covered installing, configuring, troubleshooting and administering such protocols as DNS and DHCP, TCP/IP, NWLink, and IPSec. The training covered such aspects of network protocols as remote access policies and network routing. Security was one of the topics of this exam, as well. Network security using IPSec and encryption and authentication protocols was discussed along with the network implementation details. Resource and end user management was one of the main topics of the Managing and Maintaining a Windows Server 2003 Environment exam, which also updated my knowledge of security, networking and utilities. The exam covered such topics as user creation and modification, user and group management, Terminal Services management and implementing security and software update services. Security was covered in a number of exams, including Implementing and Administering a Microsoft Windows 2000 Network Infrastructure, Installing, Configuring and Administering Microsoft Windows 2000 Server and Designing Security for   a Windows 2000 Network. All aspects of network security were covered in the various training sessions for these exams, including topics such as analysis of network security requirements in relation to organizational realities and requirements, design and implementation of such specifics as authentication policies, public-key infrastructures and encryption techniques, physical security, and design and implementation of security audit and assurance strategies. Also included were security considerations for all auxiliary services, such as DNS, Terminal Services, SNMP, Remote Installation Services and others. Implementation of Active Directory and knowledge of varied server roles was provided by the exam Designing a Microsoft Windows 2000 Directory Services Infrastructure. The training for this exam encompassed the design and implementation of an Active Directory forest and domain structure as well as planning a DNS strategy for client and server naming. This training also included design and implementation of a number of different server types, such as file and print servers, databases, proxy servers, Web servers, desktop management servers, applications servers and dial-in management servers. Further knowledge of Active Directory and auxiliary services was provided in the training for Implementing and Administering a Microsoft Windows 2000 Directory Services Infrastructure. This training included such topics as installing, configuring and troubleshooting Active Directory and DNS, implementing Change and Configuration Management, and managing all the components of Active Directory, including moving, publishing and locating Active Directory Objects, controlling access, delegating administrative privileges for objects, performing backup and restore and maintaining security for the Active Directory server via Group Policy and the Security Configuration and Analysis tool. The topics covered in CIS 332, Network Server Administration, have been completely encompassed by my previous experience, training and certification with Microsoft Windows Server 2000, as well as updated knowledge gained by   training for Microsoft Windows Server 2003. I have been constantly increasing my skills and knowledge in this area for the past six years, using both training and work experience to gain certifications which prove that I have a complete grasp of all aspects of the subject matter included in this course. Installing and configuring servers and network protocols, troubleshooting failed installations or configurations, resource and end user management, security design and management, design and implementation of Active Directory services and implementing and administering a wide variety of network server roles are all major aspects of my training and certification experience. I feel I am fully qualified for the information covered in CIS 332, and should be granted credit for this course.

Youth Crime - 1946 Words

Sociological theories of youth crime This essay will discuss the understanding of the sociological and psychological factors of youth crime. It will be agreeing and disagreeing in the above statement Youth crime is also known as juvenile delinquency, juvenile delinquency refers to criminal acts performed by juveniles. Most legal systems prescribe specific procedures for dealing with juveniles, such as juvenile detention centres. There are a multitude of different theories on the cause of crime, most if not all of which can be applied to the cause of youth crime. Youth crime is aspect of crime which receives great attention from the news media and politicians. Crime committed by young people has risen since the mid- twentieth century,†¦show more content†¦Preventing youth crime before it happens is the first and best way to protect society. The Youth Justice Strategy identifies prevention as one of its key objectives. This new approach to youth crime also acknowledges that the law is only one part of the solution  ¾ some of the most effective responses to crime lie outside the criminal justice system. Crime and disorder remains an important concern for our communities. We know this from our own experiences and our knowledge is backed by the 2000 Crime Survey, where just over 8 in 10 people identified crime as a serious problem. And although disorder may not involve criminal behaviour, the Scottish Household Survey has consistently reported that around 30% of respondents think groups of young people hanging around to be a problem in their area. Over half of families living in council flats identified this as a problem. Long-lasting strategies that address the causes of youth crime must involve a variety of individuals, organizations and governments in such areas as crime prevention, child welfare, mental health, education, social services and employment. 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Low Carb or Low Fat Diet - Free Essay Example

Sample details Pages: 10 Words: 2858 Downloads: 10 Date added: 2019/02/12 Category Health Essay Level High school Tags: Diet Essay Did you like this example? According to the CDC, (Central Disease Control and Prevention) , about 93.3 million adults suffer from obesity in America as of 2016. That is nearly 30% of the entire population of America. I say â€Å"suffer† because obesity acts and is treated like an epidemic, affecting an enormous amount of people and for their entire lives. Don’t waste time! Our writers will create an original "Low Carb or Low Fat Diet?" essay for you Create order This is so much of a burden, that the average obese adult will pay around $1,500 more in medical costs than a person of normal weight. A major cause of obesity in America is a poor choice of diet. From this observation, we can narrow it all down into one question, which is the best diet for weight loss. In searching for differentiated diets, I came across and decided to do research on two of the most common diets: low carb and low fat. Some think that this question would be an easy decision and go on to pick low carb because carbohydrate essentially means sugar, and sugar sounds culpable to weight gain, or they might pick low fat, because the word fat does not sound very nutritional or healthy to have on a diet, so they choose this diet to cut it out. It then turns into a large dispute between parties on which is a better option marginally, followed by experiments depending on weight loss over a period of time. However, it is much deeper than just weight loss and what sounds right. T he real question to be asked here is: â€Å"Which diet will help me reach my intended weight AND still keep me healthy?† Even though the main goal is to test overall health, a good basis is to see which of the diets produces the greatest percentage of weight loss. In an experiment conducted by multiple MDs of the New Balance Foundation, eight different subjects were closely monitored while on either a low carb or a low fat diet. According to the article: â€Å"?At 6 months, there are differences in percent weight loss, with low-carb diets leading in percent loss (12)†. Conversely, â€Å"low-carbohydrate diet was better with regard to dyslipidemia and glycemic control after adjustment for differences in weight loss. (12)† What this article is saying is that low carb not only produces a greater percentage of weight loss, it also deals with dyslipidemia, which is described as an abnormally elevated level of cholesterol or fats found in the bloodstream, a common symptom of obesity. What this means for low carb is that it is both efficient at producing weight loss and is an overall healthier d iet compared to low fat. One confusing distinction in dieting and bodily health is the difference between blood sugar and blood pressure. When you have high blood pressure, it is generally a side effect of diabetes and can be caused by overeating, not exercising enough, missing medicines, high stress levels, illness. Blood pressure is the force pushing against the artery walls, and if this force or pressure is too high, it can lead to a number of things, including: ?nosebleeds, headaches, or dizziness, heart attack, and a stroke. High blood pressure is more commonly referred to as hypertension. Carbohydrates include foods like grain products, such as bread, crackers, pasta and rice. These carbs break down into glucose during the process of digestion, and glucose is what fuels our body and gives us energy. Even though carbohydrates in these experiments are proven to be healthier against cutting out fats from your diet, cutting out carbs still might be unhealthy for you. Despite a low carbohydrate diet being more beneficial in regard to symptoms like dyslipidemia, cutting out carbohydrates is cutting out your source of energy. In the opinion of Harvard Medical School, by going on a low carb diet â€Å"you deprive your body of a main source of fuel — and many essential nutrients that you need to stay healthy. (2)† Many people confuse the likes of low-carb diets and low-sugar diet. A carbohydrate is either a starch, a sugar, or a fiber. What a low sugar diet is more focused on sugars that are added to a product or is referring to processed foods. Processing food is to change the form of a natural agricultural food or crop by using unnatural means. For example, potato chips are considered a processed food. This is because to create a potato chip, you would take a natural crop like a potato and cut and fry it unnaturally, creating the potato chip. A low sugar diet will commonly steer clear of these products, because they use preservatives and added sugars that are unhealthy altogether, while low carb diets demand abstinence from a wider variety of food. An article by Reid Health does not take a stance on low carb but says: â€Å"Bottom line, you should avoid added sugar, processed foods, refined grains (like white bread), sodas, other sugary drinks and sweets as much as possible. To help you loo k and feel your best, you should choose nutrient-dense, healthy carbohydrates. (5)† While carbohydrates can be made out to be beneficial in most ways, it can at the same time be detrimental to your health. An article written by Harvard Medical School explains that are unprocessed carbohydrates are the same. Specifically that â€Å"All carbohydrates turn into glucose and raise our blood sugar. But some do it faster than others. Controlling blood glucose is important for weight management as well as diabetes control. (Harvard 3)† Judging from this statement, even though carbs are the human body’s main source of energy, carbohydrate intake at the very least should be monitored because it can cause a spectrum of complications caused by high blood sugar or hypertension. For example, hypertension can cause severe damage to your blood vessels connected to your brain, resulting in a stroke. What is trying to be said here is that something as small as deciding what you eat can lead to a variety of unfavorable outcomes, sometimes resulting in serious injury or e ven death. Watching what you eat, aspects like your carb intake and the difference of refined and regular carbs. When people hear the word â€Å"fat†, it immediately receives a bad connotation in their mind. They are not entirely wrong in thinking this, as fats by general definition are described as ?a â€Å"natural oily or greasy substance occurring in animal bodies, especially when deposited as a layer under the skin or around certain organs.† This definition makes fat to be something gross or unhealthy. However, according to the MedicalNewsToday.Com article written by ?Christian Nordqvist?, fats are put into a different light. He says that â€Å"?Fat is a nutrient. It is crucial for normal body function, and without it, we could not live. Not only does fat supply us with energy, it also makes it possible for other nutrients to do their jobs. (?Nordqvist?)?† This puts a different take on what we think fats to be. If fats can be good, and are supposedly â€Å"necessary nutrients†, why are there so many low fat diets out there? Many people truly believe that anything associated with fat is a bad thing for your body. An article from Harvard Health Publishing affirms this intuition. It states that â€Å"?Eating foods rich in trans ?fats? increases the amount of ?harmful? LDL cholesterol in the bloodstream and reduces the amount of beneficial HDL cholesterol. (Harvard 13)’’ It may be confusing the difference between fats and trans fats, but put simply, regular fats are natural, trans fats are created during processing. What this article points out is that trans fats specifically raise your LDL cholesterol and gets rid of HDL cholesterol. LDL cholesterol is what puts you at risk of a heart attack and other heart problems and HDL cholesterol is beneficial because it removes harmful cholesterol from the bloodstream. Fats, in this way are more harmful than beneficial towards your body because they heighten your level of unhealthy cholesterol and increase the risk of heart attacks and complications. Most foods that we eat today commonly contain trans fats, so the spectrum of a low fat diet can be very extensive. On a low fat diet, rather than watching the specific food you are eating, you have to take it further and look into the â€Å"Nutrition Facts† of what you are eating. An informational article from the American Cancer Society shares some tips on watching your trans fat intake, saying that â€Å"?A good rule of thumb when you’re reading food labels: For every 100 calories, if the product has 3 grams of fat or less, it’s a low-fat product. This means 30% or less of the calories come from fat. (ACS 7)† This can can help you get a good idea of what you are looking for in the diet, as Nutrition Facts are available prior to purchase and are posted in most restaurants as well. This makes any low fat easier to follow, because you play less of a guessing game when you are watching what you eat. Fat, whether or not a good thing or a bad thing, is irrefutably a major source of energy. In fact, fat contains more than twice the number of calories of an equal amount of carbohydrate or protein . In health.gov’s article about low fat diets, it shows how and why you should choose a low fat diet over any other diet, not just over a low carb diet. It gives the notion that at most 30% of the total calories you get from a meal should come from fats . Using the articles specific example, â€Å"Cutting back on fat can help you consume fewer calories. For example, at 2,000 calories per day, the suggested upper limit of calories from fat is about 600 calories. (1)† It is a widely known fact that the average man needs around 2,500 calories a day, but this author uses the figure 2,000 because ?the average man needs 2000 to lose one pound of weight per week (). Connecting these two facts, cutting back on fat is one way to lose weight because cutting back on fat is also reducing the amount of calories you consume, which is necessary for losing weight. Food items that are low in fat/trans fat content include: ?egg whites or egg substitutes crab, white fish, shrimp, and light tuna (packed in water) chicken and turkey breast (no skin), or ground turkey breast (7). These items are only a handful of items still available on a fat restricting diet. Some of the more obvious option are most non-processed foods, as stated earlier that trans fats come from processing. Some of the non-food-related benefits that fat has is insulation. Fat connects to muscles with a specialized connecting tissue. This fat then insulates the body, regulating the interior temperature (Harvard 2). While this is one of the only benefits fat has when attaching to your body, it is a huge factor in what looks and actually is healthy. This means that having a bit of fat on your body is not as much of a bad thing as people make it out to be. Even though a person â€Å"looks better† or looks skinnier than another person, that heavier person might be in a healthier condition than the other because the fat is not a bad thing, all it means is that it has to do with moderation. â€Å"Too much of a good thing isn’t such a good thing†. Something to consider when comparing the two diets (low fat and low carb) is that the experiment shown earlier displayed that low carb was favorable to percent lost in weight, but showed over time showed that both diets ferred more or less the same when it came to loss percentage. Many people often confuse the concepts of dieting and eating healthy. The difference between the two is actually quite simple. Dieting usually entails that the person partaking in the diet is trying to lose weight or lessen the symptoms of obesity, such as hypertension or dyslipidemia. Eating healthy is merely trying to put your body in a healthy state. Another aspect of eating healthy is that what you can eat is not nearly as restrictive as what you can eat with a diet is. Eating healthy has more to do with spiritual and mental health and dieting focuses on physical health and appearance. The concept of eating healthy is more abstract because you are setting a broad goal for yourself and dieting is constant revisiting and reevaluating. Put simply, when you are on a diet, you are trying to follow something and when you are eating healthy, you are following yourself.(Ross 11) It is always hard to decide which diet to go on because even though you already have a goal in mind of what you want to weigh, what you want to look like, what you want to be able to do again, you might not yet understand your needs or what your body can physically handle. You might steer towards a low carb diet if you have high levels of blood sugar or hypertension, or if you have an unhealthy amount of fat or are solely trying to lose weight, you would go towards low fat because that way you reduce your calorie intake. It is very important to know what your body needs and is capable of because if you chose the wrong diet, you could end up hurting yourself. Before even considering going on a diet, you need to look at the situation from a logical standpoint. If you are 12 and under, unless you have special circumstances whereas you physically need to diet, it is probably not safe to diet because you have no idea what your body needs quite yet. When you are contemplating going on a di et, get a third and fourth opinion, typically from your doctor because their job is to make sure you are in good health. Taking all the risks and dangers is a very important aspect of making changes to your body. In conducting this research, while seemingly indecisive, I can draw from the research and knowledge that I obtained, that a low fat diet diet is a more optimal option for a diet than a low carbohydrate diet. This comes from the beneficial aspects of each diet, Low Fat clearly outweighing Low Carb in many ways. One aspect of these benefits that convinced me to draw this conclusion is the fact that Low Fat not only lowers the level of LDL cholesterol in your bloodstream, it is already helping you lose weight by reducing your calorie intake. It also seems more favorable to follow than Low Carb because it uses specific figures and numbers to follow, available in restaurants and on labeled food, while with Low Carb it is more of a guessing game. Finally, the last notion of this diet that leads me to believe that this is the better diet is that in a Low Carb diet, you are cutting your main source of energy, while in a Low Fat, you are only cutting a partial source of energy, which was nec essary to cut if you wanted to lose weight in the first place. My hypothesis is going to look similar to an experiment previously mentioned in the research. If I test multiple people on the same diet and see the effects of the diet over time, I believe that the Low Fat diet will show more of a difference health and weight wise than the Low Carb diet because of the research I have conducted. Even though the bodies of the subjects will have different abilities and different needs, the LF diet will have a more visually apparent effect that a LC diet will. References Works Cited 1. â€Å"Choose a Diet Low in Fat, Saturated Fat, and Cholesterol.† ?Chapter 6 Fats?, health.gov/dietaryguidelines/dga95/lowfat.htm. 2. Harvard Health Publishing. â€Å"Carbohydrates Good or Bad for You?† ?Harvard Health Blog?, Harvard Health Publishing, www.health.harvard.edu/diet-and-weight-loss/carbohydratesgood-or-bad-for-you. 3. Harvard Health Publishing. â€Å"The Truth about Fats: the Good, the Bad, and the in-Between.† Harvard Health Blog?, Harvard Health Publishing, www.health.harvard.edu/staying-healthy/the-truth-about-fats-bad-and-good. 4. â€Å"High Blood Pressure (Hypertension).† ?Mayo Clinic,? Mayo Foundation for Medical Education and Research, 12 May 2018, www.mayoclinic.org/diseases-conditions/high-blood-pressure/symptoms-causes/syc-2037 3410. 5. Hospital, Reid. â€Å"| Reid Health Right Beside You.† ?Reid Health?, www.reidhealth.org/carbohydrates-101-the-benefits-of-carbohydrates/. 6. â€Å"How Many Calories Should You Eat Per Day to Lose Weight?† ?Healthline?, Healthline Media, www.healthline.com/nutrition/how-many-calories-per-day. 7. â€Å"Low Fat Foods.† ?American Cancer Society,? www.cancer.org/healthy/eat-healthy-get-active/take-control-your-weight/low-fat-foods.ht ml. 8. Nordqvist, Christian. â€Å"Types of Fat: The Good and the Bad.† ?Medical News Today?, Johnson 11 MediLexicon International, 22 June 2017, www.medicalnewstoday.com/articles/141442.php. 9. â€Å"Overweight Obesity.† ?Centers for Disease Control and Prevention,? Centers for Disease Control and Prevention, 13 Aug. 2018, www.cdc.gov/obesity/data/adult.html. 10. â€Å"Processed Foods What’s OK and What to Avoid.† ?Eat Right. Academy of Nutrition and Dietetics.,? www.eatright.org/food/nutrition/nutrition-facts-and-food-labels/processed-foods-whats-ok -and-what-to-avoid. 11. Ross, Harling. â€Å"The Difference Between Dieting and Eating Healthy.† ?Man Repeller,? 16 Aug. 2018, www.manrepeller.com/2018/01/difference-between-dieting-and-eating-healthy.html ?. 12. â€Å"Authors.† Prospective versus Retrospective Studies, sphweb.bumc.bu.edu/otlt/MPH-Modules/PH/NutritionModules/Popular_Diets/Popular_Di ets_print.html. 13. â€Å"Authors.† Prospective versus Retrospective Studies, sphweb.bumc.bu.edu/otlt/MPH-Modules/PH/NutritionModules/Popular_Diets/Popular_Di ets_print.html.

Ethics Essay - 1613 Words

Ethics Imagine a 15 year old student in philosophy class. After discussing why should you or shouldnt you judge other societies, and getting in depth with ethics, the teacher decides to tell a story to the class. She says..there is a tribe in the Amazon(Brazil) were they show love and respect by cutting body parts.It would be a good sign if your father cuts a finger of a son.... she then asked the class .... if you end up in the Amazon, would you stop a father cutting a sons finger because in your society is wrong? Can you imagine how disappointed would the family be if this happens? Is that ethics?. Before getting into the†¦show more content†¦Modern scholarship follows the ancients lead in referring standardly to philosophers before Socrates collectively as Presocratics COOPER, JOHN M. (1998, 2004). Socrates . In E. Craig (Ed.), Routledge Encyclopedia of Philosophy. London: Routledge. Retrieved May 12, 2006, from http://www.rep.routledge.com/article/A108SECT1 Ethics gives you more questions than answers. Can it be, that what i think is right, its actually the opposite in another part of the world? The bottom line is that we are different.We have different cultures, we are raised with different values and that sometimes we forget we are not the only culture in the world. Ethics at work, specifically applied to employees is one of the most important tasks of the human resources team of any company. Whether its right or wrong, good or bad, it must be communicated, explained and done from the top of the management team all the way to the employees. Situations were ethics come into play are really common in any given day at work. Are we discriminating because of sex, race, and age? Can we lie to customers just to make business? The list can go on an on. Ethical situations exist and the way we manage them will have a huge impact on the results. 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The Detrimental Effects of Use of Oxygen Samples for Student

Question: Explain Detrimental Effects Of Use Of Oxygen? Answer: Introducation: Patients with acute coronary syndrome usually receive oxygen therapy during an emergency treatment. This is usually done by the paramedics before the patients first contact with a physician. However, there are recent studies that have suggested that the use of oxygen therapy may actually be doing more harm than good. This paper will look at the evidence that has brought about the changes to paramedical clinical practice in the treatment of the acute coronary syndrome. In a review published by the Cochrane collaboration (Cabello, Burls, Emparanza, Bayliss, Quinn, 2010), they analyzed the evidence from randomized controlled trials that compared the outcomes of patients who were given normal air and those who were given oxygen to breathe. The trials involved 387 patients out of which 14 died. Out of the 14 who died, three times as many people who were given oxygen died compared to those who were given normal air. The evidence suggests that oxygen could indeed be harmful and there is need to further evaluate to ensure the current practice is not harmful to individuals with acute coronary syndrome. In a recent trial report by Ranchord, Argyle Beynon (2011), they compared routine oxygen use and titrated oxygen therapy. There was only one death out of the 68patients that were treated with routine oxygen and 2 deaths out of the 68 of those treated with titrated oxygen therapy. An analysis carried out by the authors illustrate that there are other factors that could have affected the findings. According to Meier, Ebrahim, Otto, and cases (2013), the fact that these trials indicate the possibility of harm to the patients requires further investigation to ensure the safety of the patients with the acute coronary syndrome. The evidence illustrated above indicate that there is a possibility that the use of oxygen caused the deaths of the patients analyzed in the trials. It is, therefore, important to change the paramedic clinical practice in handling patients with the acute coronary syndrome. References Cabello, J. B., Burls, A., Emparanza, J. I., Bayliss, S., Quinn, T. (2010). Oxygen therapy for acute myocardial infarction. Sao Paulo Medical Journal, 128(6), 378-378. Meier, P., Ebrahim, S., Otto, C., Casas, J.P. (2013) Oxygen therapy in acute myocardial infarction good or bad? [Editorial]. Cochrane Database of Systematic Reviews2013 ;( 8): 10.1002/14651858.ED000065 Ranchord, A.M, Argyle R., Beynon R. (2011) High-concentration versus titrated oxygen therapy in ST-elevation myocardial infarction: a pilot randomized controlled trial. American Heart Journal 2012; 163(2):168-175. dx.doi.org/10.1016/j.ahj.2011.10.013