Best Practice, Best Fit and Resource View of the firms analysis for Essay

Best Practice, Best Fit and Resource View of the firms analysis for relevance - Essay Example But these new concepts definitely did one thing, forced the organizations to focus on a neglected area. Similar is the case with "Best Practice", "Best Fit" and "Resource View of the Firms". The three concepts led to awakening which provided insights into organizations themselves on practices, organizational variables and the organizational resources respectively. Best Practice term arrived in the 1990s when the entire world of organizations were in a flux, the world became unipolar and the resources deployed hitherto for anticipated war between two poles of the world were suddenly available for better use, for business and the core of business is essentially the needs, desires of human beings and the ability to fulfill them. The geographical boundaries had suddenly collapsed and the resulting globalization created aggregation of these needs, desires and the abilities to fulfill them, unleashing a state of huge threats and huge opportunities. A plethora of business concepts arrived on the scene, quickly delivered by ICT elements across a wider spectrum of organizations. Best Practice concept was one of them. The search of reasons or factors behind a superlative performance led to a process-by-process analysis and its comparison with the more successful companies. Best practice is essentially kno... It is a quest for improvement through the use of experience behind creating a better practice by others. The comparison could be either through unstructured or structured mechanisms, documents, interactions. Benchmarking encompasses best practices and best practice results are the starting point in improvement through benchmarking partnerships. Normally two organizations enter into benchmarking partnerships and try to transfer the best practices from one context to another. It starts with regular comparison of performance measures in identified areas and when it is found that performance of some parameters is quite high, a detailed analysis is carried out to find out how it is done and what is the practice being adopted to achieve that superior result. Now the key is to understand and implement that practice, leading to closer people to people interactions, depending on the understanding and enthusiasm of the implementing organization. Another approach seen regarding best practice is industry or sector specific associations engaged in sharing and exchanging of data, experiences in seminars or workshops to facilitate the growth of the industry itself through capture of best practice and disseminating the same. The approach has definitely helped organizations to see the organizational activities in terms of a set of practices and look the process-wise comparison with others. The practice of comparison came down to the level of practices. Best Practice Blues There have been significant activities in the efforts to capture best practices from other sources and deploy in the own context. There are reasons to say that these efforts did not get a fair amount of success except the possible benefit of communicating a comparative message that someone

Compare and contrast the Virginia and New Jersey plans presented at Essay

Compare and contrast the Virginia and New Jersey plans presented at the Constitutional Convention - Essay Example equitable ratio of white and other free citizens of every age, sex and condition and 3/5th of rest of the persons except Indians who do not pay taxes in each state. However New Jersey plan is more specific about confederation and states about diminishing or increasing number of states as well. According to Virginia Plan, the national legislature may have the power of legislation by confederation in all cases where individual states are incompetent or harmony of the country may be at stake. It may have the right to negate all laws that are passed by states found contravening to articles of Union. New Jersey Plan offers the same power in addition to the authority to promote commerce, impose levy and raise revenues in the states. According to both plans, executive may negate any legislation that may not be passed by 2/3rd of national legislature. Executive may have powers to execute national laws to appoint to offices or impeach for certain reasons. New Jersey Plan states the same powers for executive in addition to directing military operations without taking any command over troops. Comparing both the plans, it is specifically found that New Jersey plan offers more power to the new federal government because it focuses to revisit and enlarge articles of confederation and gives executive authority to direct military operations as

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

The Beginnings of the Soviet Union Essay examples -- Soviet Union Euro

The Beginnings of the Soviet Union The United States that we live in makes it very hard for us to fathom what a struggling nation is like to live in. In the United States, we are socialized to believe that America is the most superior of all the countries and our prosperity will continue to grow. We are very fortunate to be born into a relatively high standard of living as a society, thus we cannot comprehend what it is like for countries trying to build societies from the bottom up. John Scott portrays this brilliantly in his book "Behind the Urals" as he examines individual people and their struggles as they worked in Magnitogorsk. These citizens worked in the most inhumane conditions, all with the intention to help their country develop under the new system of the Soviet Union. The Soviet Union had just gone through an entire turn around in their political, social, and economic spheres as they went from one extreme to another. The old Czarist government was always out to serve the rich landowners, while treating th e peasantry as second-class humans rather than equals. However, when the Russian Revolution came to a head, and the Red Communists or Bolsheviks defeated the White Czarists, Russia was left with an entirely new system of thought in its government. This ideology viewed the working class and peasantry as the main citizens in their society, while the rich landowners were not nearly as powerful as they once were. Thus the workers of Magnitogorsk held a very important position as they had the responsibility to help the Soviet Union take flight as a country that could compete with other powerful countries of the world, all while working under the most inhumane conditions. John Scott moved to the Soviet Union leaving the United States and in his eyes, its unsatisfactory capitalistic way of governing. Scott may have been aided in making his decision as he saw the United States slip into the Great Depression, a time when the conditions in America reached an all time low. He left his roots in the United States to begin a new life in a foreign country simply because he was disgruntled with American governing and was appealed to by the Soviet philosophy of governing. It tool Scott a tremendous amount of will and fortitude to leave behind everything he knew so well, to start a new life on the other side of the world. He showed his courage as he began... ...derwent, he served his sentence with dignity and was respected as one of the best workers there. In the beginnings of the Soviet Union, and more specifically Magnitogorsk, a diversified group of people from various ethnic, religions, and national backgrounds all put forth their individual efforts to develop the new Russia. The grueling environment that these people lived in developed them into strong and proud workers. In looking to our home front, I cannot find one example that even borders similarities to life in the Soviet Union under Stalin's Five Year Plans. We can study the times, even look to experts in the field for information on the topic, but we can never fully grasp the extreme environment that the peoples of Magnitogorsk lived in. They jeopardized and sometimes even sacrificed their own lives to build up a country. Lives were not lost in the battlefields, but instead on the job as workers froze from the climate while working the blast furnaces. The Soviet Union's success is usually given to the Communist ideology or even Stalin, but instead it was the hard workers w ho came from all over the eastern hemisphere to take on and complete the task of developing Russia.

Thorn Queen Chapter Eight

Kiyo was gone the next morning, as I'd suspected he would be. We'd stumbled inside to my little-used bedroom once it started raining, and his side of the bed was cold, telling me he'd left some time ago. I sighed, trying not to let the knowledge of him being with Maiwenn get me down, and headed out to see what was going on in Queen Eugenie's domain. The first thing I picked up on was that everyone was really excited that it had rained. We'd returned to normal sunny conditions this morning, but last night's rain had brought the land to life. Cacti bloomed. The trees seemed stronger. And while there were no ostensible signs of excess water, I could sense it in the ground and even slightly in the air. Had having sex caused it? Maybe. Maybe not. Regardless, I was pleased with my good deed. I made motions to leave, but Rurik stopped me. â€Å"Don't you want to question the prisoners?† I paused. What I wanted was to go home, shower, and change into clean clothes. â€Å"Can't you do that?† I asked. He frowned. â€Å"Well, certainly, but†¦Ã¢â‚¬  But it should be my job. That was the unspoken message. I suspected Aeson would have never done such a thing. He would have left it to thugs. I knew if I delegated it to Rurik, he'd do it without (much) complaint. There was something in his eyes, though, that told me he expected more of me than an ordinary monarch. I'd never expected to gain such regard from him-or to feel so uneasy about it. Rurik had pissed me off to no end in the past, but suddenly, I didn't want to disappoint him. â€Å"Okay,† I said. â€Å"Let's do it.† I'd interrogated plenty of monsters, gentry, and even humans in my day. But there was something weird about interrogating prisoners. It was strange enough to learn that I actually had a dungeon in the castle. There were even shackles on the wall, but thankfully, our two prisoners weren't bound. They were a man and a woman, both ragged and sullen. He looked my age; she looked older. I entered the bronze-barred cell, Rurik and another guard behind me. I crossed my arms over my chest and swallowed my misgivings. I was Eugenie Markham, badass shaman and slayer of Otherworldly miscreants. This was no different from any of my other jobs. â€Å"Okay,† I told the prisoners, my voice harsh. â€Å"We can make this easy or hard. Answer my questions, and it'll go a lot faster and smoother for all of us.† The woman glared at me. â€Å"We don't answer to you.† â€Å"That's the funny thing,† I said. â€Å"You do. You're in my land. You're under my rule, my jurisdiction.† She spat on the ground. â€Å"You're a usurper. You stole the land from Aeson.† Considering the way power was always shifting in the Otherworld, I found that statement ludicrous. â€Å"Everyone's a usurper here. And in case you haven't heard, I didn't steal the land from him so much as blow him up.† Her face remained hard, but I saw the slightest flicker of fear in the guy's face. I turned to him. â€Å"What about you? You going to be reasonable? Are you going to tell me where the girls you kidnapped are?† He nervously glanced at his companion. She gave him a hard look, its message easily interpretable: Don't talk. I sighed. I didn't want to resort to torture. All-powerful ruler or not, it was just an ugly thing I didn't want to dirty my hands with. I had a feeling my iron athame pointed at their throats would go a long way to get them to communicate. Instead, I opted for another solution. Producing my wand, I stepped away from the others and spoke the words to summon Volusian. The momentary cold descended upon us, and then the spirit stood before me. Rurik and the guard were growing accustomed to this, but the prisoners gasped. â€Å"Volusian,† I said. â€Å"Got a task for you.† â€Å"As my mistress commands.† I gestured to the prisoners. â€Å"I need you to put muscle on them. Get them to talk.† Volusian's red eyes widened slightly, the closest he ever came to looking happy. â€Å"But you can't kill them,† I added hastily. â€Å"Or hurt them-much.† The pseudo-happiness disappeared. â€Å"Start with the guy,† I said. Volusian sidled across the cell and was only reaching his hand out when the guy cracked. â€Å"Alright! Alright! I'll talk,† he cried. â€Å"Stop, Volusian.† The spirit stepped back, his glum expression growing. â€Å"I don't know anything about girls disappearing,† the man said. â€Å"We aren't taking them.† â€Å"You've been preying on people,† I pointed out. â€Å"And girls have been vanishing near your base of operation. Seems kind of suspicious.† He shook his head frantically, eyeing Volusian warily. â€Å"No, it's not us.† â€Å"Have you heard of them disappearing?† â€Å"Yes. But it's not us.† His words were adamant. â€Å"Yeah, well, I find it hard to believe they're all running off. If it's not you, then who is it?† â€Å"You're a fool,† the woman snapped. â€Å"What would we do with a group of girls?† â€Å"The same thing men usually use girls for,† I replied. â€Å"We can barely feed our own people! Why would we take on more mouths to feed?† That was kind of a good question. â€Å"Well, you still haven't really given me another explanation.† â€Å"We heard a monster's doing it,† the man blurted out. â€Å"A monster,† I repeated flatly. I looked over to Rurik who simply shrugged. I turned back to the prisoners. â€Å"Any details on this monster?† Neither responded. It was strange, particularly considering how some prejudiced part of me still regarded most gentry as dishonest, but I believed them about not taking the girls. I thought the monster explanation was bullshit, but they might honestly have believed it to be true. Volusian took a step forward without my command, and the guy hastily spoke. â€Å"The monster lives in our land. In the Ald-Thorn Land, that is.† â€Å"How do you know that?† I asked. â€Å"Because only girls from the Thorn Land have disappeared,† the woman said. â€Å"Westoria borders the Rowan Land, and two of their villages are very close. Skye and Ley. But they've had no one go missing.† â€Å"You guys seem to know a lot about this for allegedly not being involved.† â€Å"We don't need to be involved. We raid both sides of the border-word gets around.† She spoke of her raiding as a matter of pride, and I tried not to roll my eyes. â€Å"Okay. Let's put the girls on hold. Where did the fire demons come from?† No answer. I sighed again. â€Å"Volusian.† Volusian swiftly moved forward again and wrapped his hand around the guy's throat. Most spirits had little substance, but with his power, Volusian was as solid as any of us, his touch cold and deadly. The man screamed and crumpled to the ground. â€Å"Stop! Stop!† yelled the woman. â€Å"I'll tell you.† I halted Volusian and looked at her expectantly. The man remained on the floor, rubbing his throat and moaning. The skin on his neck bore bright red marks. The woman looked angrier than ever. â€Å"It's our leader who summons them. Cowan.† â€Å"You expect me to believe some vagrant has that kind of power?† I asked. â€Å"Why isn't he off working for a noble?† â€Å"He was a noble, one of Aeson's advisors. He preferred to live a rough life, rather than work for someone like you.† â€Å"Aeson did have a noble named Cowan,† Rurik said. â€Å"Her story isn't implausible.† I suddenly felt weary. None of these were the answers I wanted. No leads on the girls, and now I had a rogue noble who could summon demons. â€Å"Okay,† I said. â€Å"That's all I've got for now.† â€Å"What are you going to do with us?† the woman demanded. â€Å"Another excellent question,† I murmured. â€Å"Aeson would have killed them,† said Rurik. â€Å"And you know I'm not Aeson.† Would setting them free accomplish anything? Much of what they'd done had been from hunger and desperation, not that that justified robbing and potentially killing and kidnapping. If I freed them out of guilt, I doubted they'd learn their lessons and go on to become upright citizens. I certainly wasn't going to kill them, though. I didn't even want to hold them in this cell much longer. The guard who'd accompanied Rurik cleared his throat. â€Å"Your majesty, you could sentence them to a work detail.† â€Å"A work detail?† â€Å"There are others like them, other criminals, who serve a term doing labor as punishment for their deeds.† â€Å"Like digging your aque†¦whatever,† said Rurik. That didn't sound so bad. And hey, it might actually be useful. I gave the order and was assured the two prisoners would be transported to their work site. The whole thing felt a little strange. Here I was judge, jury, and-if I chose-executioner. No one argued with my decision. No one questioned the time I set-six months. Although, Rurik's arched eyebrow made me think he would have sentenced them to life. â€Å"Okay,† I said when we'd emerged out of the lower levels of the castle and I'd sent away Volusian. â€Å"Now I'm going home.† Shaya suddenly rounded the corner. â€Å"There you are,† she said anxiously. â€Å"I've been looking for you.† â€Å"I'm leaving.† Her face turned confused. â€Å"But Prince Leith is here to see you.† â€Å"Who†¦oh.† The image came back to me. The moderately cute guy from the party. The Rowan Queen's son, who hadn't been all that annoying. â€Å"Why is he here?† â€Å"After your last visit, I dispatched those with any affinity for metal out to search for copper. They found a lot of it-thought it's been difficult to extract-and I sent out word that we'd be in a position to set up trade for it soon. Leith is here to negotiate on behalf of his mother.† â€Å"Man,† I said. â€Å"You guys move fast.† Her looked turned wry. â€Å"Well, yes, but there's also the fact that you invited him to visit sometime. He's taking you up on the offer. In fact, I suspect seeing you is more important than the trade negotiations.† â€Å"Good thing. Because I'm not so good in the way of negotiations.† I never wore a watch and had left my cell phone back in Tucson. I had no idea what time it was, only that I was spending more and more time in the Otherworld. Seeing Leith was only going to delay me further. â€Å"I'll see him. But it's going to be fast.† Shaya looked relieved. I think she'd worried I would bolt, which was a very good fear to have. As we walked to the chamber Leith was waiting in, she gave me a curious look. â€Å"Perhaps you'd†¦like to change and clean up first?† I looked at my clothes. They were pretty badly wrinkled, and I didn't doubt that I had grass in my hair from last night. â€Å"No,† I said. â€Å"The less appealing he finds me, the better.† Unfortunately, that proved impossible. When we entered the room, Leith leapt up, face aglow with delight. â€Å"Your majesty! It's so wonderful to see you again.† He swept me a half-bow and kissed my hand. â€Å"You look amazing.† He was apparently into the grunge look. â€Å"I hope you don't mind me arriving like this. When my mother heard the news of your find, she wanted to make sure we could get in on it as soon as possible.† â€Å"Sure,† I said, taking my hand back. â€Å"No problem.† The room was a comfortable parlor that still bore the signs of Aeson's tastes in decorating. Tapestries, lots of velvet, and dark colors. Everyone waited for me to sit on one of the plush sofas and then followed suit. I made a point of kind of sprawling on mine. It wouldn't have been out of the range of gentry etiquette for Leith to come snuggle up beside me. As it was, he was still beaming at me and seemed a bit put out when Shaya jumped right in. â€Å"So, your highness. We'd like to discuss trading our copper for your wheat.† As they began to talk, I had a sudden flashback to that god-awful board game my mother used to make me play, Pit. I let my mind wander as the two of them hashed out the finer details of matters I didn't entirely understand. My thoughts drifted to some upcoming jobs I had, the mystery of the demons and the missing girls, and of course, Kiyo. Always Kiyo. Leith and Shaya wrapped up their negotiations fairly quickly. From the happy look on her face, I took it our team had come out ahead. With a polite bow in my direction, Shaya rose, holding some papers to her chest. â€Å"If you'll excuse me, I'm going to have these written up and formalized so that the prince can sign them before he leaves.† I took this as my cue to entertain him, but nothing readily came to mind. I couldn't really talk to him about reality TV or American politics. Finally, lamely, I said, â€Å"Thanks for your help. I mean, with the trade and everything.† He grinned. â€Å"We're getting as much out of it as you. Maybe more.† â€Å"Shaya didn't seem to think so,† I said, speaking without thinking. This made him laugh. â€Å"She's a good negotiator. You're lucky to have her.† He leaned forward. â€Å"Especially since I'm guessing this really isn't your†¦well, let's just say it's not one of your normal pastimes.† The frankness caught me by surprise. I'd expected him to remain starstruck and silly, like most of the guys around here who wanted to hit on me. Leith's current expression wasn't lecherous or adoring now, just knowing and sympathetic. â€Å"No, it's really not. This is a kind of a big life change.† â€Å"And yet, you knew you'd be taking this on when you defeated Aeson.† I hesitated. Both Shaya and Rurik had hinted to me on a number of occasions that I really shouldn't elaborate on the totally unexpected-and unwanted-nature of my queen-ship. Even if I hadn't fought Aeson with the specific intent of supplanting him, the point remained now that I was stuck with this. Coming across as weak and whiny to those outside my inner circle could create more problems. â€Å"Well, yeah,† I said brightly. â€Å"We just didn't anticipate this many problems when the land changed.† â€Å"But this is how your world is?† â€Å"The part I live in. But we've had a long time to get used to it and figure out ways to survive and get water in. I gave Shaya books on how to construct some of that stuff, so hopefully she'll find someone to do it.† His brow furrowed. â€Å"Is there any way I could take a look? I might able to help.† For a moment, I wondered if this was his new ploy to schmooze me-until I recalled what Shaya had said about him having a brilliant mind for technology, inasmuch as the gentry could. If he could parse diagrams and whatnot, it might be worth getting closer to him. â€Å"Sure,† I said. â€Å"We could certainly use it.† He smiled again, and as it lit up his face, even I could acknowledge he was pretty good-looking. Not like Kiyo, of course. Or even†¦well, like Dorian. But pretty cute. â€Å"I'll set to it as soon as I can. If there's anything else I can do to make this easier for you, I'll do it.† There was an enraptured look on his face. Yeah, he definitely had a crush, but he didn't irritate me in the way so many other more obnoxious suitors did. An odd thought occurred to me. â€Å"Leith†¦here's something you might be able to help with. Have you ever heard of girls disappearing from the Rowan Land? In the areas that border my land?† The look on his face showed that this was the last question he'd expected from me. â€Å"I†¦beg your pardon?† â€Å"Girls have been disappearing from my land, right near your borders.† What were those names? â€Å"Skye and Ley. But the people I talked to say nothing's happening to your girls. Do you know anything about this?† He shook his head, utterly confused. â€Å"No†¦I'm afraid I don't know very much about the lives of those people.† Leith's words weren't contemptuous by any means, but there was an implication that villagers and peasants just weren't people he associated with. It reminded me of Rurik's comments about how Aeson would have never troubled himself to investigate bandits or missing girls unless they directly affected him. Leith wasn't as much of an asshole as Aeson, but he and his mother were likely just as out of touch as any other noble. I think a fair amount of disappointment must have shown on my face because he suddenly grew eager to make me feel better. â€Å"But I swear, I'll look into this when I return. I'll ask Mother, and we'll send messengers out to report back. I'll find out everything I can for you.† I smiled at his enthusiasm. â€Å"Thanks, Leith. It's really great of you to help.† â€Å"Helping a pretty queen is no trouble at all. By the way, have you ever thought about getting a crown?† We talked a little longer, and I found he actually was a really nice guy, given to moments of humor and intelligence. It wasn't enough for me to jump into bed with him, but I appreciated finding someone else to connect with in the Otherworld. Shaya returned at last with the paperwork-hand-printed on scrolls, of course-and while Leith signed, we got a hold of the engineering books for him. His eyes widened with delight, and I swear, he probably could have sat down and started reading then and there on the floor. Instead, he took the hint that I had other things to do, and after many more compliments and hand kisses, he took his leave. â€Å"You've given him another open invitation,† Shaya pointed out. â€Å"Yeah, I know. But he's harmless. I like him.† â€Å"None of them are harmless, your majesty.† I couldn't entirely tell if she was joking or not. â€Å"Well, it'll be worth the hassle if he can solve our water problem and help with the girls.† â€Å"The girls?† I gave her a quick recap of my interrogation with the prisoners. Her face turned thoughtful as she processed my words. â€Å"Skye and Ley†¦Ã¢â‚¬  â€Å"Do you know those towns?† She nodded. â€Å"They and Westoria are configured in a way that places them equidistant from a gateway. A crossroads.† â€Å"What, to my world?† She nodded again. â€Å"Huh. I wonder if that's a coincidence. I wonder†¦I wonder if it's possible that†¦Ã¢â‚¬  One of my crazier ideas came to me. â€Å"Do you think those girls could be leaving and going to my world?† â€Å"I don't know. Shining ones do often cross over. It's not unheard of.† â€Å"Yeah, I know. To cause trouble. Or to steal women.† I had to fight a scowl on that one. My own mother had been one such woman, abducted and forced to be my father's mistress. â€Å"You think these girls are going to go kidnap guys so they can have kids?† The easy ability to conceive was why so many humans got kidnapped. Usually, it was gentry men taking human women. Shaya's smile turned wry. â€Å"I somehow doubt it would come to that. Women have been known to cross over, spend time in your world, and return pregnant. They don't need to bring the men back.† Fair point. Well, this was certainly a weird development. I'd have to wait and see what Leith reported back, but I supposed if these girls weren't actually being abducted†¦well, there was little for me to do. Admittedly, I'd always fought adamantly against gentry sneaking to the human world, but I wasn't sure where the right and wrong of this situation lay. â€Å"I guess that'd be easier to deal with than a monster taking them. Still leaves that stupid demon problem.† I sighed. â€Å"Well, one issue at a time, I guess.† â€Å"Are you leaving now?† â€Å"Yes. Finally. Thanks for handling this today.† â€Å"Of course,† she said. She actually sounded like she meant it. Her pleased expression turned momentarily hesitant. â€Å"Although†¦there's something you should know. Someone else responded right away to the trade offer.† â€Å"That's good news.† â€Å"It's Dorian.† â€Å"Oh.† Of course Dorian would respond. How could he stay away from an opportunity to put me at his mercy? â€Å"You can deal with it, though, right?† â€Å"Well, that's just it. He's specifically requested that you talk to him. At his home.† â€Å"What?† I stared. â€Å"He†¦he can't do that.† That wry smile of hers returned. â€Å"He's a king. He can do anything he wishes.† â€Å"Yeah, but Leith came here! Dorian just wants me to go to him so that he can taunt me.† And no doubt flaunt Ysabel in front of me. â€Å"Leith's kingdom needs copper more than Dorian's. I suspect Dorian is doing this as a personal favor to you.† â€Å"That's not exactly how I'd put it.† She shook her head, the amusement now warring with exasperation. â€Å"I know there's tension between you, but I suspect if you could be nice to King Dorian, he might make us a very generous deal. One that could help us immensely.† A generous deal. The Oak Land was flourishing. I didn't doubt they had all sorts of food and other items we could use. I thought about those poor people in Westoria and even about my prisoners who'd spoken of having too many mouths to feed. I sighed. â€Å"Fine. I'll talk to him. And I'll even be nice.† I started to turn away, needing more than ever to get back to my own home. Then I glanced back behind me. â€Å"But Shaya? Just to be safe, you might want to keep looking for more trade partners.†

Just A Pot Of Basil

At the age of eight one of my favorite things to do was dream about living in a time where gigantic beasts loomed over the earth. Form the gigantasaurus to the brontosaurus I enjoyed anything from the Precambrian period. I grew to appreciate the monstrous creatures even more after I took my first trip to the Carnegie Museum of Pittsburgh. I had never seen such elaborate displays of marvelous full-scale dinosaurs, since I was accustomed to seeing them no larger than the height of a book or television screen. I recall roaming through the many displays pretending that I was one of them. Usually, I pretended to be the Troodon, a species that is thought to have the largest brain in proportion to the rest of its body. Even though I was smaller than the rest of the dinosaurs, I always knew that I could outsmart them if I was a clever Troodon. Of course I would forget that they had been extinct for millions of years, as the plaques in front of the enormous exhibits reminded those who were tall enough to read them. But I carried on in my world of dinosaurs while I was in the museum, free to dream as I cared to. The distance and time between the real dinosaurs and I disappeared when I was in the museum, in my little world. Therein lies the significant difference between seeing and imagining, and being told or influenced, that is, being mystified. Mystification, as the art critic John Berger in Ways of Seeing explains, is the process of explaining away what might otherwise be evident (Berger 112). I was instantly captivated from the moment I saw the tied-together skeletons stretching as high as my own house; should I have cared about the petty details that would have distracted me from my own imagination? Original paintings are silent and still in a sense that information never is (Berger 125). The skeletal remains of ancient beasts strung up give only a portion of what such creatures really were millions of years ago. The color of their skin, the texture of their bodies, or even the size of their internal organs are just a few of the endless questions that remain unanswered, lost over time. But museums give something more than any book could ever tell, and that is the real life experience of seeing what could never be perceived otherwise. When life breathed through the dinosaurs they were never frozen into a perfect stance like they are portrayed in museums. Our imagination allows us to fathom what it really may have been like, but the past remains where it is, and can only at its best be relived in movies or museums or our imagination. Museums have never made me feel awkward or uneasy, they come as second nature to me. I enjoy being enveloped by a different emotion each time I look at the skeleton of a dinosaur, or see a mummified pharaoh, or even a beautiful painting of a landscape. I have always been able to let everything go, and be consumed by a striking or stunning image. The wonderful thing about museums is that every few feet there lies an artist waiting to draw you into their world. Artists and their works contained within a single building span over centuries and continents. All contain different points of view and expresses it to the best of their abilities. Today we see the art of the past as nobody saw it before. We actually perceive it in a different way (Berger 112). History meets in a museum, and constantly forms new accounts through time. Each day that passes we have gained something which may add to our overall perception of the world around us. This is why Berger claims that we see things differently and therefore there exists no definitive account of exactly the way things were at any specific moment in time. It is lost forever, and at best, can only be saved in an altered form. There is something magical about the power of the atmosphere of a museum. The silence is filled with a sea of thoughts running through viewers minds. When I first saw John White Alexander s painting Isabella and the Pot of Basil I was immediately captivated. Even my first glance told me that there was something more to the large pot in the painting than meets the eye. In a painting all its elements are there to be seen simultaneously (Berger 121). What the eye can perceive in an instant may take pages to explain. There lies the beauty of art. One glance at Alexander s work captivated me instantly. There lives some hidden secret inside the woman s soul that lay next to the pot. And sure enough, the small plaque beside the painting described a story that told me that my assumptions were correct. The painting was written as a reflection of a poem written by John Keats. Here, briefly, is the story of Isabella and the Pot of Basil. Isabella had two brothers that expected her to marry a well-endowed man so they could collect a significant dowry from her marriage. But Isabella never married, and fell in love with a carpenter named Lorenzo, who was working for brothers. The two were madly in love, and visited each other frequently whenever they were certain that no one could find them together. Soon though, a brother learned of their secret, and the two brothers took Lorenzo into the woods, killed him, and buried him in a shallow grave. One night while Isabella was wailing in bed over the mysterious disappearance of her supposed runaway love, Lorenzo’s ghost came to her and described the occurrences and location of Lorenzo s body. Isabella went to Lorenzo s grave, cut off his head, and took it back home with her where she put it in a big basil pot and covered it with moss, soil, and basil seeds. She watered the seeds with rose water and her own tears and talked to her basil until it grew incredibly lush. After her brothers stole her basil pot, Isabella died of misery and heartbreak, singing a song about the loss of her basil and love. Alexander was able to condense this entire love story into a single painting. Without having read the 500-line poem or at least having some knowledge of the story, the average viewer would never have guessed that her lover s head was contained in the pot. The emotions contained within Isabella and her sacred pot reach beyond words. The pain that she felt consumed her to the point of her own death, where no words can exist. Depicted in the painting is not just a sad woman, but a woman who is about to die, sick and miserable with heartbreak, love, and loneliness. The meaning of an image is changed according to what one seen immediately beside it or what comes immediately after it. [It] is distributed over the whole context on which it appears (Berger 123). Only after reading the small plaque beside the work and continued research after visiting completed my perception on the almost life sized piece of art. These important clues added to what I could deduce from the painting. Without them I would merely have seen just a pot of basil and a woman lying next to it. History is a mystery that is continually being investigated. Without knowing the past no deductions can be made of the present. Alexander captures Isabella in a moment of perfect stillness. Perhaps she is already dead in the artist s eyes, lying beside her love, their souls reunited. The barren space below the pot could contain the spiritual body of Lorenzo. Alexander seems to have purposefully left the open space on the right side of the painting for his spirit next to her. Isabella has her eyes closed and her hand is gingerly extended. Her two fingertips brush against the side of the pot, as if she s imagining the pot to be his face. Her neck appears slightly extended as if she were giving the curved pot a gentle kiss. The stench that must have emanated from the pot would have been almost unbearable to others, yet somehow the power of love caused Isabella to ignore all reason and sanity as her soul sought for her love and mercy. White flowers contrast with the overall melancholy of the image yet also add just the right touch of beauty, innocence, and peace. There are several of these flowers directly under the pot and another at the base of Isabella s feet. This white represents the purity of their love that was so terribly destroyed by her evil brothers. The tear of her garment on her right shoulder shows her distress and her apathy towards her self-appearance. Isabella s soul can be at ease once she is reunited with her beloved Lorenzo; her physical condition no longer matters. There are of course many other paintings depicting Isabella and the Pot of Basil, but none seemed to capture the emotion as well as Alexander does. His art is powerful, captivating, and entices the viewer to look deeper, to learn more, and to almost feel the emotions raging through the canvas. The moment I saw the painting, I knew that there was more to it. The stillness that Alexander recreates reaches beyond words, and required only the same silence in return. The way we see things is affected by what we know or what we believe When in love, the sight of the beloved has a completeness which no words and no embrace can match (Berger 106). Perhaps my heart goes out to Isabella, for I myself am in love and can reconcile with what she may have felt. Even if Isabella was just a fictional character for both Keats and Alexander the emotional consequence of such a painting is undeniable. The love between a man and a woman knows no end, and its eternity continues through people of all time and nations. Of course we are all granted different perspectives, but there lies a central burning passion about love which can only be depicted as a fraction of its entirety. Thus, love in fact, [closes] the distance between the painting of the picture and one s own looking at it (Berger 125). The research that I completed on Isabella and the Pot of Basil introduced a different and more in depth perspective on the work. Without reading the corresponding poem, I would perhaps have seen only a woman standing next to her favorite pot, and be left to imagine what more was involved. My intuition told me that there was more to the painting than what first met my eye. The observations and assumptions that I made based on the picture and poem are based completely my own deductions and learned assumptions that I have acquired throughout my life. Therefore, if John Berger had looked at this image in the same atmosphere as I did, he could have seen something completely different. Therein lies the truest beauty of art, for art is capable of capturing and recreating a moment lost in time without regard to the opinions of those who will see it. Art is beautiful often because we make it beautiful. Big ugly dinosaurs are certainly not beautiful to most, but to me as an eight-year-old, they most definitely were. Being told what is beautiful and what meaning lies behind a painting is the epitome of mystification. According to Berger this lends [undeserved] authority (121) to the artist. The image now illustrates the sentence (Berger 122). And thus, whatever thoughts a viewer has conjured about a painting or work of art are lost, negated, or skewed, yet it provides a strong basis for interpretation. The painting by Alexander exemplifies the poem by Keats. In many instances, poetry is associated with a visual image, but provides only the framework from which a perception of an image can be formulated. Words help set the tone, yet can never deter from the heart of work. I prefer to say that sentences help to illustrate an image. And John Berger would most certainly agree that there is much more to Alexander s work than just Isabella and a pot of basil.