How did the cotton economy shape the Souths environment and labor Assignment

How did the cotton economy shape the Souths environment and labor system - Assignment Example How did the Lecompton Constitution and the Lincoln-Douglas debates affect the debate over slavery in the territories? Why did Lincoln’s election in 1860 cause the South to secede? Scholarly sources which were researched online were used in providing for the answers. a. The antebellum South was mainly agricultural with cotton as their main product and with the unprecedented growth of the cotton economy during that period, slave labor became the major capital investment. South remained agricultural and produced on site goods and services. It remained a largely closed society and only a few towns or villages emerged. (â€Å"The Cotton Economy in the South.† American Eras. 1997). Slaves were only regarded as properties by their owners and were often maltreated and oppressed. Though a few had the courage to fight back, their punishments were much more severe; they were whipped, beaten, drowned or hanged. Others resisted by slowing down in their work or feigning illness or breaking their tools. Others sabotaged their production like setting fire on the crops, and some resorted to theft of food, tobacco, liquor, and money from their slave masters. In the 1850s, slaves in plantations dwelt in quarters made up of crudely-made cabins. They lived together within the same homestead and this made the black communities flourish. Within these slave communities, they were able to retain their African culture with their folk tales, religion and spirituality, music and dance, and language, and they had their own families. These made their lives as slaves bearable. b. The Americans believed they had a â€Å"manifest destiny† to expand across the Pacific Ocean. After the election of James K. Polk in 1844, he at once initiated the annexation of Texas and he also eyed California and New Mexico. The Indians made it more difficult for the Americans in their war against Mexico. As U.S. was having war with Mexico, they were also having a cultural war

Law assignment Example | Topics and Well Written Essays - 1000 words

Law - Assignment Example Owing to this autonomy conferred upon the provincial authorities, many employment laws have been developed and applied within individual provinces. On the basis of this, the arguments made in this paper relate to the employment law, with specific reference to the common law, Ontario Human Rights Code (HRC), Employment Standards Act (ESA) and Pay Equity Act (PEA). In this paper I argue that the employment law, beginning with the Common Law up to the statutes that currently govern it has not always attempted to strike a balance among the rights of employers, employees and society in general. I have made reference to relevant statutes; their foundations in Common Law, and analyzed their overall impact on the welfare of employees, employers and the general society. Cognizant of the fact that not all parties are equally appreciative of the legal provisions of employment, this essay focuses on what implications the above stated legal provisions, statutes and judicial precedents have had on the parties. There are numerous attempts to strike a balance between the needs of employees, employers and expectations by society. However, these attempts have at times been hindered by compelling disparities between different laws that the courts should rely on to make sound decisions. In this respect, judges have at times had to overrule certain legal provisions in order to uphold more acceptable thresholds of determination. These disparities in legal provisions that govern the same aspect of employment form the basis of my argument that the laws have not always attempted to uphold a balance among the parties. To the extent that they difference in content, these laws can be considered as objects of perpetuating inequality in law, as each law with a flawed perspective hurts a party to a case while benefitting the other unnecessarily. For instance, we

Classic Knitwear Essay Example for Free

Classic Knitwear Essay Classic Knitwear, founded in 1995, began production of a unique line of unbranded casual knit apparel. Included in their product line were such clothing as T-shirts, sport shirts, sweatshirts and other wearing apparel. Although the company saw exceptional revenues as of 2005, they still felt that they were not meeting certain criteria when it came to their gross margin. They sought to increase their gross margin, currently sitting at 18%, to that of a more comfortable number of 20%. To combat this issue, Classic Knitwear decided to team up with Guardian, a producer of odorless repellant protection against bugs, and combine their fortes into a line of clothing infused with the bug repellant technology. These new products would hopefully to rise the gross margin to the 20% they were hoping to accomplish. The non-fashion casual knitwear market consisted of products that range from casual t-shirts to even underwear. Within this industry, it can be divided into two categories, those manufacturers who brand their products with their name and those companies who choose not to brand their line of products. On the branded side of the industry, Classic competed with three major brands. These brands were JamesBrands (which accounted for $4.5 billion in revenue from sales), Flowerknit (which accounted for $1.25 billion in revenue from sales), and Greenville Corporations TopTops Division (which accounted for $630 million in revenue from sales). These branded labels competed on the level of private- labeled businesses. On the other side of the industry, Classic competed with one company in terms of unlabeled products. BB Activewear were major competitors as they generated $590 million or 23.6% market share, which made them a leader in the market. Although not directly involved within this sector, Jamesbrand, Flowerknit and Greenville Corporations TopTops Division still were involved with Classic on this level. Distribution channels are essential when it comes to the wholesales of these companies products. 90% of the product distribution from these companies go directly to two distinct types of retailers. Almost 50% of these sales are accounted for from mixed retailers, such as Wal-Mart and Kohls, who sell clothing as well as wide variety of other products. The other 40% is sold towards clothing specialist retailers, such as Gap and Brooks Brothers, who only specialize in the selling of clothing related products. The remaining 10% of the distribution channels contained bits from non- grocery retailers, home shopping, internet retailing and direct selling to the customers. In order for manufacturers to compete for retail business, they used a variety of strategies in order to gain attention from these retailers. Some of these tactics involved prices, variety of products, and efficiency of delivery. Classic Knitwear, since its inception, has been a simple manufacturing company whose focus is on creating and distributing unbranded casual knit apparel which includes T-shirts, sweatshirts and fleece like products. Unlike other companies that chose to have expensive products which carried prestigious fashion labels, Classic decided to venture away from them and focus on products that were categorized as non- fashioned knitwear. With this strategy, Classic accounted for $550 million in revenues from domestic sales. They have also decided to sell only in the United States, as foreign markets were too much of a risk that could have negative consequences. 75% of this revenue came from the selling of their products to wholesalers, who in turn, resold the Classic clothing to screen- print channels which customized the products with logos and images. Ortiz and Chong decided to concentrate on this pathway because it offered the fastest growth potential than trying to sell like ordinary retailers. As a result, Classic Knitwear had established itself as the #2 seller in the market, accounting for 16.5% of the market share. Classic generated the remaining 25% of their revenues from mass retail channels under private labeling. Classic would sell their products to retailers such as Wal-Mart and Dollar General and would be carried under the name of the retailer or through a house brand that was developed by the retailers themselves. In fact, these two retailers accounted for 57% of those revenue sales. To help accomplish such high revenues, Classic had to achieve low production costs throughout the entire company. To ensure that such goals were obtainable, Classic established state-of-the-art production factories that were situated off shore, mainly in the Dominican Republic. Being situated not in the United States allowed them to have much lower production costs than those produced domestically. Although other companies had also set up production factories in other countries, Classic was able to have a slight competitive advantage over these other companies. What helped them keep this competitive advantage was a high volume- low SKU (stock keeping unit) strategy. This ensured that they would produce high quantities of products without the large variety of products that other companies had. As of 2005, Classic felt that it would never reach their goal of 20% gross margins through various controlled labels or tie in promotions. However, Classic Knitwear had an epiphany which could potentially shoot their gross profits to levels that they would feel satisfied with. With the rise of the West Nile virus across the Americas, more and more people were looking for ways to prevent the transmission of the diseases. Classic thought it would generate the attention of customers to produce a new line of clothing that would be infused with chemicals that would be able to repel insects that carried the West Nile virus. With the help of another company, Guardian, who specialized in insect repellants, they would be able to create such a line of products. The reason that they chose Guardian was due to their flagship repellant, have established them as one of the top producers in insect repellant. The products would consist of a short and long sleeve T-shirt, a Mens polo, and a Mens fleece. Along with the production of these chemical infused clothing, Classic was targeting males 18-35, seeing as these individuals would most likely be outside during times when insects are active. The initial investment of such a line could cost about $10 million, which would help to generate 50% unaided awareness across the United States. In order to get the needed awareness of their product out to the public to ensure increased gross margins, Classic relied heavily on marketing. They had studied how other brands that were selling similar brands of insect repellant clothing and how they were successful, establishing themselves into small niche markets. Based on those already established companies, Classic decided to sell their product lines to retail stores with cardboard displays housing the different styles of shirts. On the outside of each of the boxes would display pictures of outdoor related activities that would promote the proper use of each shirt. Some of these retail stores would be outdoor related stores such as Bass Pro Shops and L.L. Bean. Classic wanted to have 10,000 displays in stores over the next 2 years after the product line was to begin production. To help get these displays in stores, they offered discounts on the sale of T-shirts if the store agreed to have a display in their store. Classic, with the production of these chemical infused shirts, could have a possible juggernaut to help generate sales, but there could be other possibilities that could help them reach their target gross margin of 20%. One alternative would be to not produce the new line of shirts, relying on frequent customers to help generate the extra sales to gain the extra gross margin. Another possibility would be to vertical integrate with one of the screen-pressing companies that create the logos which are later screened onto the sold shirts. By integrating, they could possibly cut unnecessary costs that would also help create higher gross margins. Lastly, another possible alternative to this problem would be to establish a brand of clothing that is positioned near the high labeled brands. They would have to compete with the big three companies with sales, but could possibly steal sales away from them to help establish themselves. Classic Knitwear was set with a problem of what to do to try and earn more in their gross profits. To solve such case, it would be recommended that they continue with the production of these insect repellant shirts. With the outbreak of the West Nile virus and outdoorsmen wanting styled brands to wear, this idea would help to generate the sales need to raise the gross profits. Based on Consumer.com surveys, it was concluded that there was a strong desire for such a product, especially one whose clothing was made out Classics materials. In the end, the continuation of this line would help generate the extra gross margin they had hope to gain.

Multilevel Thresholding According to Histogram

Multilevel Thresholding According to Histogram Make Multilevel Thresholding According to Histogram by Cooperative Algorithm based on AFSA and Fuzzy Logic Image segmentation is a technique which is usually applied in the first step of image analysis and pattern recognition and is an important component of them. This technique is taken into account as one of the most difficult and the most sensitive problems in image analyzing. In this paper, a cooperative algorithm is proposed based on AFSA and k-means. The proposed algorithm is used to make multilevel thresholding for image segmentation according to histogram. In the proposed algorithm, first, artificial fish (AF) perform optimization process in AFSA. After swarm convergence, obtained cluster centers by AFs are used as initial cluster centers of k-means algorithm. After forwarding AFSAs output to k-means, AFs are reinitialized and performs clustering again. The proposed algorithm is used for segmenting 2 well-known images and obtained results are compared with each other. Experimental results show that segmented images quality by the proposed algorithm is much better than four other t ested algorithms. Keywords: Multilevel Thresholding; Histogram; Cooperative Algorithm; k-means. Image segmentation is a technique which is usually applied in the first step of image analysis and pattern recognition and is an important component of them. This technique is taken into account as one of the most difficult and the most sensitive problems in image analyzing. In fact, quality of final result of image analysis depends highly on the quality of image segmentation result. In image segmentation process, an image is divided into different regions. Segmentation approaches of mono-color images are with respect to discontinuity and/or similarity of gray level amounts in one region. If the approach performs segmentation based on discontinuities, the image is segmented with respect to abrupt changes on gray level by means of recognizing dots, lines and edges [1].The purpose of image segmentation approaches is to classify and convert pixels into regions. Histogram thresholding is one of the techniques, which has been applied extensively in mono-color images segmentation [2]. Generally, images are composed of regions with various gray levels. Therefore, an images histogram can consist of some peaks that each of them is related to one region. To separate boundaries of two peaks from each other, a threshold value is considered between valleys of two adjacent peaks. Indeed, histogram thresholding is a famous technique which is looking for peaks and valleys in a histogram [3]. Various clustering algorithms such as k-means [4] and FCM [5] have been used for histogram thresholding so far. As a matter of fact, clustering approaches, because of simplicity and effectiveness, belong to the most famous techniques that could be used for natural image segmentation. Applying clustering algorithms in histogram thresholding are such that first colors histogram is built and after that, clustering is done according to color distribution among pixels. O ne of the clustering methods is to use such swarm intelligence algorithms as particle swarm optimization (PSO) [6], and artificial fish swarm algorithm (AFSA) [7]. PSO was presented by Kenedy and Eberhart in 1995 [8]. Different versions of this algorithm have been used many times in data clustering [9]. Artificial fish swarm algorithm (AFSA) was presented by Li Xiao Lei in 2002 [10]. This algorithm is a technique based on swarm behaviors that was inspired from social behaviors of fish swarm in nature. AFSA works based on population, random search and behaviorism. This algorithm has been applied on different problems including machine learning [11, 12, 13], PID controlling [14], image segmentation [16], data clustering [7, 16] and scheduling [17]. K-means or famous Lloyd algorithm is one of the famous data clustering algorithms [18]. This algorithm is of high convergence rate, but has some weaknesses such as sensitivity to initial values of cluster centers and convergence to local op tima. Researchers have tried to remove these weaknesses by hybridizing this algorithm with other algorithms such as swarm intelligence ones [6, 19] and to utilize their advantages. One of these algorithms is KPSO in which first, k-means is performed and after that outcome of k-means is delivered to PSO as a particle [20]. Hence, at the beginning of the algorithm, k-means reaches to a local optimum with its high convergence rate and after that PSO takes the responsibility of increasing the result accuracy and exiting form local optimum. In this paper, a cooperative algorithm is proposed based on AFSA and k-means. The proposed algorithm is used to make multilevel thresholding for image segmentation according to histogram. In the proposed algorithm, first, artificial fish (AF) perform optimization process in AFSA. After swarm convergence, obtained cluster centers by AFs are used as initial cluster centers of k-means algorithm. After forwarding AFSAs output to k- means, AFs are reinitialized and performs clustering again. In fact, in the proposed algorithm, AFSA is used for a global search and k-means is used for a local search. The proposed algorithm along with four other algorithms is used for image segmentation on two known images Lenna and Barbara. Efficiency comparison shows that the proposed algorithm has an appropriate and acceptable efficiency. The remainder of the paper is organized as follows: in sections 2 and 3, standard AFSA and k-means algorithm will be described respectively and in section 4, the proposed algorithm will be presented. Section 5 studies the experiments and analyzes their results and final section concludes the paper. In water world, fish can find areas that have more foods, which is done with individual or swarm search by fishes. According to this characteristic, artificial fish (AF) model is represented by prey, free-move, and swarm and follow behaviors. AFs search the problem space by those behaviors. The environment, which AF lives in, substantially is solution space and other AFs domain. Food consistence degree in water area is AFSA objective function. Finally, AFs reach to a point which its food consistence degree is maxima (global optimum). In artificial fish swarm algorithm, AF perceives external concepts with sense of sight. Current position of AF is shown by vector X=(x 1, x 2,à ¢Ã¢â€š ¬Ã‚ ¦, x n). The visual is equal to sight field of AF and Xv is a position in visual where the AF wants to go. Then if Xv has better food consistence than current position of AF, it goes one step toward X v which causes change in AF position from X to Xnext , but if the current position of AF is better than X v, it continues searching in its visual area. Food consistence in position X is fitness value of this position and is shown with f(X). The step is equal to maximum length of the movement. The distance between two AFs which are in Xi and Xj positions is shown by Dis ij =||X i-Xj|| (Euclidean distance). AF model consists of two parts of variables and functions. Variables include X (current AF position), step (maximum length step), visual (sight field), try-number (the maximum test interactions and tries) and crowd factor ÃŽÂ ´ (0 The standard k-means algorithm is summarized as follows: Initial position of K cluster centers is determined randomly. The following steps are repeated: a) for each data vector: data vector is allocated to a cluster that its Euclidean distance from its center is smaller than the other clusters centers. Distance from cluster center is calculated by Equation (1): (1) In Equation (1), Xp is data vector p, Zj is the center of cluster j and d is the number of dimensions of data vectors and cluster center vectors. b) After allocating all data to clusters, each of cluster centers is updated by Equation (2): (2) Where, nj is the number of data vectors that belong to cluster j and Cj is a subset of all data vectors which belong to cluster j. The resulted cluster center of Equation (2) is the average vector of data vectors comprising cluster. (a) and (b) steps are iterated until the stopping criterion is satisfied. In this section, the proposed algorithm is described. In the proposed algorithm, there exists a population of AFSAs AFs. This population of AFs is initialized randomly in problem space. Each AF consists of K cluster center positions in one dimensional image histogram space. Therefore, search space for AFSA for K cluster centers has K components. Fitness function which AFSA has to minimize is shown in Equation (3). (3) Clustering on histogram is done by Equation (3) based on color distribution between given images pixels. The image is divided into K clusters (Ci) according to color attribute by K-1 thresholds. In Equation (3), the distance between color Xj on image histogram and the center of a cluster which it belongs to ( Zi), is multiplied by the frequency of pixels (fj) which have color value Xj on given image. This value is computed for all color values with respect to the center of a cluster which they belong to. Each color becomes the member of a cluster in which their distance from that cluster center is less than other cluster centers. Finally, the obtained results of all clusters are summed with each other. Indeed, Equation (3) calculates sum of intra cluster distances for one dimensional gray scale images, which is one of the most well-known clustering criteria. For improving obtained results by AFSA, some modifications must do on its structure. The best found position by swarm members so far in AFSA is saved in bulletin and AF which has found it might go even toward worse positions with performing a free-move behavior. Therefore, AFs cannot utilize their best swarm experience for improving the convergence rate because they just save it in bulletin. On the other hand, performing free-move behavior is inevitable for maintaining diversity of the swarm. In this paper, to remove this problem, every AF except best AF can perform free-move behavior. In fact, during execution of the proposed algorithm, this behavior is not performed for the best AF of the swarm at all. Hence, the best found position by the swarm would be the position of the best AF of the swarm. As a result, other members of the swarm can move in the direction of the best found position by executing follow and swarm behaviors. The purpose of designing the proposed algorithm is to take advantages of both AFSA and k-means algorithms and remove their weaknesses. K-means is of high convergence rate, but its very sensitive to initializing the cluster centers and in the case of selecting inappropriate initial cluster centers, it could converge to a local optimum. AFSA can pass local optima to some extent but cannot guarantee reaching to global optima. However, AFSAs computational complexity for optimization process is much more than k-means. How the proposed algorithm functions remove weaknesses of these two algorithms and apply their advantages is as following: In the proposed algorithm, first, the AFs are initialized in AFSA. Each of AFSA contains K cluster centers (K-1 threshold) which are displaced in the problem space by performing AFSAs behaviors. AFSA continues to perform until the AFs converge. After convergence of AFSA, best AFs position including the best cluster centers which have found by AFs so far is considered as the input of k-means. Then, k-means algorithm starts working and while it is not converged, it continues working. Therefore, AFSA searches globally and as far as it can, it passes local optima. After convergence of AFSAs AFs, its output would have an appropriate initial cluster centers for k-means. Hence, after sending AFSAs outcome to k-means, this algorithm starts searching locally. Consequently, in the proposed algorithm, global search ability of AFSA has been used and after converging, a great part of optimization process will be given to k-means to utilize high capability of local search of this algorithm and its high convergence rate. Since initial cluster centers for k-means are obtained by AFSA and k-means is used for local search, k-means weakness of sensitivity to initial cluster centers is removed. But, AFSA capability may not be enough for preventing from being trapped in local optima. If this algorithm is trapped in local optima, it cannot present proper initial cluster values to k-means. Thereafter, according to low ability of k-means in passing local optima, the obtained result cannot be acceptable. To raise this problem, after convergence of AFSA, the output of this algorithm is sent to k-means. Simultaneously with starting of k-means, AFSAs AFs are initialized and start global search again. In fact, in one time of executing the proposed algorithm, AFSA has several times of chance to perform an acceptable global search. It should be noted that in the proposed algorithm, in each time of executing AFSA, AFs just search globally and converge after a short time and k-means undertakes the remaining of optimization process which is local search. Therefore, with respect to low computational complexity of k-means, huge amount of computations for local search is prevented. In the proposed algorithm, it has been tried to utilize this conserved computation load for giving new opportunities to AFSA in order to perform an acceptable global search in at least one of given opportunities to it. Hence, for each execution of global search by AFSA, k-means is also performed once. In the proposed algorithm, to determine the convergence of artificial fish swarm, the difference of obtained results in consecutive iterations of performing the algorithm is used. When particles converge, the obtained results difference in consecutive iterations decreases, so by considering a threshold for the difference between best AFs fitness values in iterations i and j, it can determine their convergence. In the proposed algorithm, because AFSA and k-means algorithms are performed multiple times , always, it has to save the best found cluster centers by algorithm so far. For this purpose, a blackboard is applied that each time k-means finishes after convergence of AFSA, the obtained result of that will be compared with saved result in blackboard. If obtained cluster centers are better than saved result in blackboard, saved value in blackboard is updated. K- means execution finishes when after two consecutive iterations of its execution, cluster centers wouldnt be displaced. Pseudo code of the proposed algorithm is represented in Figure (1). Experiments are done on two known gray scale images, Lenna and Barbara, of sizes 512*512 in Figure (2). In this paper, the well-known criterion of uniformity is used to compare images segmentation qualitatively [3] which is shown in Equation (4) (4) Where, c is the number of thresholds. Rj is the segmented region j. N is the total number of pixels in the given image, fi shows the gray level of pixel I,  µi is the mean gray level of pixels in jth region, finally, fmin and fmax are the minimum and maximum gray level of pixels in the given image, respectively. Usually, uà Ã‚ µ[0, 1] and larger amount for u declares that the thresholds are specified with better quality on the histogram. Proposed Algorithm: 1:for each AFi 2:initialize xi 3:Endfor 4:Blackboard = arg [min F(Xi)] 5:Repeat 6:for each AFi 7:Perform Swarm Behavior on Xi(t) and Compute Xi,swarm 8:Perform Follow Behavior on Xi(i) and Compute Xi,follow 9:if F(Xi,swarm) à ¢Ã¢â‚¬ °Ã‚ ¥ F(Xi,follow) 10:then Xi(t+1)= Xi,follow 11:Else 12:Xi(t+1)= Xi,swarm 13:Endif 14:Endfor 15:if swarm is converged 16:then Execute k-means on XBest-AF until stopping criterion of k-means is met 17:Endif 18:if F(Xk-means) à ¢Ã¢â‚¬ °Ã‚ ¤ F(Blackboard) 19:then Blackboard = Xk-means 20:reinitialize AFSA 21:Endif 22:until stopping criterion is met Figure (1): Pseudo code of proposed algorithm. The proposed algorithm along with standard AFSA, PSO algorithm, hybrid algorithm called KPSO [20], and k-means is used to segment two images, Lenna and Barbara. PSO and KPSO parameters are adjusted according to [6], and for k-means, initializing Forgy method is applied [21]. AFSA parameters and are adjusted according to [7]. AFSA settings in the proposed algorithm are the same as [7]. With respect to various experiments, if fitness value relating to Best AF is less than 0.1 in 3 iterations, it means that artificial fish swarm is converged. The following results are obtained from 50 times repeated experiments. Figure (3) shows segmented images, Lenna and Barbara, by the proposed algorithm with 5 and 3 thresholds. Figure 2: Orginal gray level Lenna (left) and Barbara (right) images Figure 3: The thresholded images of Lenna and Barbara using 5, and 2-level thresholds, from top to bottom. Average uniformity obtained from 5 algorithms on two images with thresholds 2, 3, 4 and 5 are shown in Table (1). As it is observed in Table (1), obtained results from the proposed algorithm is better than the other algorithms for all cases. AFSA algorithm has the worst result for all cases because of low ability in local search. K-means algorithm has found better results than AFSA because of high capability of k-means in local search. The reason for superiority of k-means to AFSA is the problem space property in histogram clustering. In fact, because of low dimensions of problem space in this environment, local search ability is of greater importance than global search ability. Also, it can reduce k-means weakness of sensitivity to initial values by means of one of the initializing methods of k-means like Forgy. Thereafter, with respect to considerable superiority of k-means local search ability in contrast to AFSA, k-means results are better than AFSAs. TABLE I: Comparison of uniformity for the five Algorithms Image T AFSA K-means PSO KPSO Proposed method Lenna 2 0.9138 0.9634 0.9730 0.9728 0.9775 3 0.9361 0.9749 0.9781 0.9783 0.9795 4 0.9495 0.9762 0.9816 0.9811 0.9826 5 0.9517 0.9804 0.9835 0.9834 0.9838 Barbara 2 0.9758 0.9761 0.9765 0.9768 0.9781 3 0.9783 0.9802 0.9808 0.9805 0.9820 4 0.9797 0.9834 0.9843 0.9851 0.9862 5 0.9822 0.9849 0.9855 0.9850 0.9884 Obtained results from PSO are better than k-means in all cases and its because of global search ability superiority of PSO to k-means. Moreover, in PSO, theres a trade-off between global search and local search abilities [16] and PSO also can perform a proper local search beside an acceptable global search. KPSO results are better than k-means results for all cases because after executing k-means in this algorithm, PSO algorithm is performed and improves obtained results from k-means. But obtained results from KPSO are not better than PSO for all cases. The reason is that sometimes k-means converges toward a local optimum and obtained result from that is not appropriate. Therefore, PSO is responsible for taking out the result from local optimum; however, it sometimes may not be successful. Indeed, improper result of k-means causes fast convergence of particles to local optimum. Obtained results from the proposed algorithm are better than other algorithms in all cases. The reason is u sage of strategies which have been used for global search in this algorithm. In fact, the proposed algorithm is successful in finding the global optima in most runs and can prevent final result from being trapped in local optima, whereas, this ability is observed less in other algorithms and they cannot guarantee passing local optima. This weakness causes that other algorithms to be of less robustness and not to be able to reach to almost the same results in their various implementations. Also, in the proposed algorithm, k-means algorithm performs local search after finding global optimum region by AFSA. Consequently, with respect to high ability of k-means in local search and taking proper initial cluster centers from AFSA, local search is done well in the proposed algorithm, too. As a result, both k-means and AFSA algorithms abilities are utilized in the proposed algorithm and the weakness of k- means algorithm cant decrease the algorithms efficiency. As it is observed in all algo rithms except KPSO, with rising up the number of thresholds, uniformity amount is improved. In KPSO, since the weakness of k-means has an undesirable effect on PSO efficiency, obtained results are not stable. In this paper, a new cooperative algorithm based on artificial fish swarm algorithm and k-means was proposed for image segmentation with respect to multi-level thresholding. In the proposed algorithm, AFSA performs global search and k-means is responsible for local search. The process of the proposed algorithm is such that the robustness and ability of preventing from being trapped in local optimums is improved. The proposed algorithm along with four other algorithms is used for segmenting 2 well-known images and obtained results are compared with each other. Experimental results show that segmented images quality by the proposed algorithm is much better than four other tested algorithms. [1] R. C. Gonzalez, and R. E. Woods, Digital image processing, In: Pearson Education India, Fifth Indian reprint, 2000. [2] S. Arora, J. Acharya, A. Verma., and K. Panigrahi, Multilevel thresholding for image segmentation through a fast statistical recursive algorithm, In: Journal on Pattern Recognition Letters 29, pp. 119125, 2008. [3] Maitra. M, A. Chatterjee, A hybrid cooperative-comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding, In: Journal on Expert System with applications 34, pp. 1341-1350, 2008. [4] M. Mignote, Segmentation by fusion of histogram-based k-means clusters in different color spaces, In: IEEE Transactions on Image Processing, 2008. [5] X. Yang, W. Zhao, Y. Chen, and X. Fang, Image segmentation with a fuzzy clustering algorithm based on Ant-Tree, In: Journal of Signal Processing 88, pp. 2453-2462, 2008. [6] Y. T. Kao, E. Zahara, and I. W. Kao, A hybridized approach to data clustering, In: Journal on Expert System with Applications 34, pp. 1754-1762, 2008. [7] D. Yazdani, S. Golyari, and M. R. Meybodi, A new hybrid approach for data clustering, In: 5th International Symposium on Telecommunication (IST) , pp. 932937, Tehran, 2010. [8] J. Kennedy, and R. C. Eberhart, Particle swarm optimization, In: IEEE International Conference on Neural Networks, 4, pp. 1942 1948, Perth, 1995. [9] A. A. A. Esmin, D. L. Pereira, and F. 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Nestle :: essays research papers

Nestle (Brief Overview) 1.  Ã‚  Ã‚  Ã‚  Ã‚  Unconventional methodology of extension to other countries in it’s early years. 2.  Ã‚  Ã‚  Ã‚  Ã‚  Nestle made a name for itself with an experiment involving a child who was intolerant to his mother’s milk or any other substitutes. Nestle not only saved the baby’s life but achieved the feat with a formula developed with a formula that included lactose as one of it’s key ingredients. 3.  Ã‚  Ã‚  Ã‚  Ã‚  Several acquisitions along Nestle’s timeline would further accent its touch in its major revolution in the food industry. CASE1 : IMPORTANT FACTS OF THE CASE. 1.  Ã‚  Ã‚  Ã‚  Ã‚  Nestle’s commencement in 1866 by the Swedish pharmacists and further expansion into Europe and subsequently the rest of the world 2.  Ã‚  Ã‚  Ã‚  Ã‚  Nestle’s landmark acquisituions. 3.  Ã‚  Ã‚  Ã‚  Ã‚  Nestle’s first mover strategy. The writer makes a comparison to enterprises during the industrial revolution. These companies had to invest in infrasture that are almost negligible in todays commerce activities, to start off production. Nestle had to engage in activities with a potential high risk such as their milk collection process in china. 4.  Ã‚  Ã‚  Ã‚  Ã‚  It’s broad based globalization that attracted 99% of it’s revenue from outside of it’s home country in a little over a century 5.  Ã‚  Ã‚  Ã‚  Ã‚  The employment of tactics and strategy in a saturated market like Europe in the late nineties. ( I.E. potential to find an emerging market way before it gets prosperous. Responses to income levels) 6.  Ã‚  Ã‚  Ã‚  Ã‚  Nestle focused more on customization instead of the then resounding and domineering globalization. They believed in customizing a product to suit a local niche one market at a time. That way new product failure rate remained minimal and New product Development grew significantly. This process is referred to as local adaptation by the writer. CASE2 : With regards to emerging markets 1.  Ã‚  Ã‚  Ã‚  Ã‚  Nestle has always pioneered in being the first mover into a new market. 2.  Ã‚  Ã‚  Ã‚  Ã‚  Aligned with the vision of its proprietor they configure new products to their best fit assumption and then introduce it to a unsuspecting market. 3.  Ã‚  Ã‚  Ã‚  Ã‚  After having a substantial leverage on the market, subsequent growth in the market would also mean growth for the subsidiary market share of nestle. 4.  Ã‚  Ã‚  Ã‚  Ã‚  Examples of such products experimented with include tofu, noodles and of Nestlà ©Ã¢â‚¬â„¢s favorite, condensed milk. 5.  Ã‚  Ã‚  Ã‚  Ã‚  Nestle noticeably pierces the market with staple or basic fodd items before upgrading to more upscale products. CASE 3: What is required for the strategy to work 1.  Ã‚  Ã‚  Ã‚  Ã‚  A centralized organizational goal and focus on new product development accented with the regional taste. 2.  Ã‚  Ã‚  Ã‚  Ã‚  An unbiased commitment to optimum product standards. 3.  Ã‚  Ã‚  Ã‚  Ã‚  Subsequent monitoring and alterations as required 4.  Ã‚  Ã‚  Ã‚  Ã‚  Progress report and full disclosure to stakeholders involved to determine if it is worth continuing.

Reader Reaction to John Cheevers The Swimmer Essay -- Cheever Swimmer

Reader Reaction to John Cheever's The Swimmer One of the main ideas that is conveyed in John Cheever's The Swimmer is the way in which life consists of different mental stages and how they each affect the consciousness of the mind. In The Swimmer, Neddy goes through different swimming pools and this represents the different journeys in his life. He progresses from boundless optimism to endless despair as the seasons go by. The times when Neddy is in or out of the water also represents the emotions he is going through and perhaps can correlate to the emotions felt throughout the duration of ones life. For example, when Neddy is not swimming, he tends to feel down or aggravated. During this sad period, he is usually in search of alcohol. Even after he has had a drink or two, he is always ready to go back into the pools, which shows a lot about Neddy's ambitious character. Neddy's journey through the pools is longer than an afternoon. In fact, we see this when he mentions the "storm passing" and the season change is shown through the phrase "red and yellow leaves." When Neddy finally reaches home, he is tired and weak. This displays aging through life and how one becomes fatigued easily as life goes on. When he sees that no one is home, it is obvious that Neddy's journey has come to an end and it seems as though Neddy has died, because his home can symbolize the heart and the soul, and since no one is home, Neddy's heart and soul is dead. Although The Swimmer and the recent American film "A Beautiful Mind" both have differing plots, their main characters have some commonalities. Russell Crowe, the young mathematician who becomes a natural code ... ...xpected of him with his wife and kids having left him. It was clear that Neddy was also annoyed at this point in time, however before he blew up at Mrs. Biswanger, she suddenly became nice to him once again. This part in the story goes to show how cruel society can be, and instead of helping out Neddy in his poor state, he is disrespected and furthermore, abandoned. It is also evident that friends can be betraying and deceiving and that one can never be too sure about their status. I feel that the society in which Neddy is living is quite shallow and irrational. Neddy is an unfortunate character and his treatment from Mrs. Biswanger shows her nature of thinking that friends are expendable which in my opinion is quite irrational. It also shows that the status in ones culture weighs heavily on their association with certain people.

Express and Courier Industry

Global express and courier industry overview Introduction The primary business of the express industry is the delivery of time-sensitive shipments, typically with a transit time of two to three days. These are delivered mainly by air and ground. The industry has been witnessing significant growth as a result of the constant rise in demand for express and courier services over the last decade across user industries, including manufacturing, pharmaceutical, financial services and high-tech sectors. In the last decade, the advent of e-commerce business models and their subsequent penetration along with increasing demand from the financial services sector contributed to strong industry growth. Most companies provide an additional range of activities besides pure transportation, including online tracking of shipments, online payment collection and insurance facilities. However, in recent times, the industry has witnessed a deceleration in growth as a result of rising fuel costs and reduced demand from user industries due to the global economic slowdown. Major players Traditionally, due to low demand and high costs of operation, the express industry has been dominated by national postal departments. While some of them still continue to be dominant in their respective national markets, others have evolved into larger regional and global players with multi-modal operations such as Deutsche Post World Net (which also operates DHL) and La Poste. Globally, the industry is dominated by the big four — Deutsche Post World Net (DHL), FedEx, United Postal Service (UPS) and TNT, all of which have strong multi-modal arms with worldwide operations. Key markets The US is the single largest market for express services, followed by Europe and the Asia–Pacific region. In recent years, the Asia-Pacific region has experienced tremendous growth, largely attributed to increased demand for express and courier services in China and India as well as sustained demand growth in Japan, Korea and Australia. Strong economic growth and increased international trade due to manufacturing outsourcing from the western countries has particularly resulted in the fast-growing demand for express and courier in India and China. Key challenges Rising fuel prices: Rising fuel costs have been the biggest concern of the express and courier industry globally over the past two years. Rising fuel costs have affected the industry directly via an increase in input costs and also indirectly by hampering growth prospects due to decreased demand from user industries. Environmental concerns: As part of the transport industry, the express industry has also come under renewed pressure from both environmental groups and governments to lower its carbon footprint. Environmental laws have necessitated increased usage of vehicles run on alternative fuel to transport and deliver shipments across the globe. DHL, UPS and FedEx have begun to deploy vehicles run on alternative fuel for deliveries and collection, especially in the mature US market. Outlook Over the past two years, the global express market has been reeling under the pressure of rising fuel and transportation costs and decrease in demand from the western countries. However, demand for express services is set to rise over the medium term with sustained growth of the Chinese and Indian economies. Over the longer term, the industry is expected to witness the emergence of large integrated players in the emerging markets of China and India. The market is expected to witness consolidation activities and a shift toward third-party logistics (3PL) and fourth-party logistics (4PL) players.