The Effects of Cocaine Research Paper Example | Topics and Well Written Essays - 1750 words

The Effects of Cocaine - Research Paper Example Statement of the Problem In order to effectively fight cocaine addiction among various members of the population, nongovernmental as well as governmental organizations have to comprehend the different reasons why people in different economic brackets choose to abuse the drug. In addition, they have to find ways of gaining the trust of drug users in order to influence their choices. Hypothesis Crack cocaine, which comes in the form of rock crystals, is considered to be the most addictive of all types of cocaine. It has become easily accessible to individuals in all socioeconomic brackets. Crack is a variety of cocaine that is currently more widely abused. Crack has more intense as well as swift effects than do the other varieties of cocaine which are injected or snorted. Crack is also cheaper to produce and thus has become accessible to people in all socioeconomic brackets. In most cases, people use crack to boost their abilities in a competitive world in which there is the constant r ace to be the best. While imbuing them with the strength to keep performing, cocaine also gives its users an abnormal feeling of pleasure. In the past three decades since it first emerged in the 80s, crack cocaine has left many destroyed communities in its wake all over the world. Research Questions 1. How does cocaine affect the physical body? 2. 2. How does cocaine affect a person psychologically? 3. What are the economic impacts of cocaine? 4. What are the medicinal uses of cocaine? 5. Who are the largest producers of cocaine? 6. What are the programs that can help a person addicted to cocaine? Crack is more pure and therefore considerably more addictive than cocaine which is mixed with impurities. Addicts who smoke crack experience a feeling of happiness in about 10 to 15 seconds while those addicted to cocaine who experience a rush 10 to 15 minutes after smoking. This feeling is then followed by a feeling of desperation when the drop into depressed feelings follows the â€Å"high.† This crash then compels the addict to seek for more cocaine so that he or she may experience the feeling of happiness once more. Consuming any amount of cocaine that is more than 100 milligrams can result in erratic, bizarre, or violent behavior. The addict will experience physical symptoms such as chest pain, blurred vision, fever, nausea, convulsions, muscle spasms, and finally death from brain or heart failure which causes the addict to stop breathing (Lennard-Browne 65). Crack cocaine addiction is an extremely difficult habit to stop and may actually require the hospitalization of th e addict who experiences adverse withdrawal symptoms upon stopping to use the drug. Psychological Effects Crack cocaine triggers major pleasure centers in the brain and brings about an extremely heightened feeling of ecstasy. People who wish to start using cocaine merely do so in order to stimulate themselves to be at their best so that they can work harder and longer. While the results of the pleasant and invincible feelings appear to give the addict an almost supernatural experience at first, repeated cocaine use soon dominates his or her life to the extent that he or she cannot function without it. Depression is the result of long term abuse of cocaine. The addicted person takes crack in order not to feel depressed. The drug reduces a person’s mental capacities to psychosis and auditory hallucinations. Crack cocaine brings about a severe mental

ALL ART A PRODUCT OF ITS TIME CULTURE'S VALUES FOUND EMBEDDED IN ART Essay

ALL ART A PRODUCT OF ITS TIME CULTURE'S VALUES FOUND EMBEDDED IN ART - Essay Example In fact, all forms of art may be viewed as products of their time and manifestations of the values of the culture to which they belong. With that said, it is only logical to conclude that the dominant mood of a period can actually be seen in any human production and an art form may be analyzed to reveal the historically defining values of a culture. Defined as "the practice of applying color to a surface" such as canvas, paper, wood, lacquer, glass, or concrete, painting is one art form worthy of analysis. The term "painting" when used in an artistic sense means the use of the craft along with composition, drawing, and other aesthetic factors so as to showcase the "expressive and conceptual intention of the practitioner." (Painting) Throughout history, painting is used as a way to represent, document, and express all the various intentions and subjects that are as many as the practitioners of the activity itself. Because of this, paintings can be representational and naturalistic as in a landscape or still life painting; abstract; photographic; loaded with symbolism, narrative content, emotion; or political in nature. (Painting) Spiritual concepts and motifs actually dominated in the history of painting--from mythological figures on pottery to biblical scenes on the interior walls and ceiling of The Sistine Chapel, as well as vivid depictions of human beings as spiritual subjects. (Painting) Oil painting, the process of painting with pigments bounded by a medium of drying oil, such as linseed oil in early modern Europe, is considered by many as a distinct painting genre "with rich and complex traditions in style and subject matter." (Painting) In fact, oil paintings throughout history can be considered as outstanding visual documentations of history, culture and lifestyle of people. And with time, as new techniques and styles have emerged, oil paintings have become more versatile and enriched, and the

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. Araujo, Study of different approach to clustering data by using the particle swarm optimization algorithm, In: IEEE Congress on Evolutionary Computation, pp. 18171822, Hong Kong, 2008. [10] L. X. Li, Z. J. Shao, and J. X. Qian, An optimizing method based on autonomous animate: fish swarm algorithm, In: Proceeding of System Engineering Theory and Practice, pp. 32-38, 2002. [11] D. Yazdani, S. Golyari, and M. R. Meybodi, A new hybrid algorithm for optimization based on artificial fish swarm algorithm and cellular learning automata, In: 5th International Symposium on Telecommunication (IST), pp. 932-937, Tehran, 2010. [12] D. Yazdani, A. N. Toosi, and M. R. Meybodi, Fuzzy adaptive artificial fish swarm algorithm, In: 23 th Australian Conference on Artificial Intelligent, pp. 334-343, Adelaide, 2010. [13] J. Hu, X. Zeng, and J. Xiao, Artificial fish swarm algorithm for function optimization, In: International Conference on Information Engineering and Computer Science, pp. 1-4, 2010. [14] Y. Luo, W. Wei, and S. X. Wang, The optimization of PID controller parameters based on an improved artificial fish swarm algorithm, In: 3rd International Workshop on Advanced Computational Intelligence, pp. 328-332, 2010. [15] C. X. Li, Z. Ying, S. JunTao, and S. J. Qing, Method of image segmentation based on fuzzy c-means clustering algorithm and artificial fish swarm algorithm, In: International Conference on Intelligent Computing and Integrated Systems (ICISS) , pp. 254- 257, Guilin, 2010. [16] L. Xiao, A clustering algorithm based on artificial fish school, In: 2nd International Conference on Computer Engineering and Technology, pp. 766-769, 2010. [17] D. Bing, and D. Wen, Scheduling arrival aircrafts on multi- runway based on an improved artificial fish swarm algorithm, In: International Conference on Computational and Information Sciences, pp. 499-502, 2010. [18] J. A. Hartigan, An overview of clustering algorithms, In: New York: John Wiley Sons , 1975. [19] C. Y. Tsai, and I. W. Kao, Particle swarm optimization with selective particle regeneration for data clustering, In: Journal of Expert Systems with Applications 38, pp. 65656576, 2011. [20] D. W. der Merwe, and A. P. Engelbrecht, Data clustering using particle swarm optimization, In: Congress on Evolutionary Computation, pp. 215-220, 2003. [21] E. Forgy, Cluster analysis of multivariate data: efficiency vs. interpretability of classification, In: Biometrics 21, pp. 768, 1965

Feeding the Ghost Essay -- essays papers

Feeding the Ghost WE ARE BETTER The novel Feeding the Ghosts, by Fred D'Aguiar, exploits the terrible conditions black people were put through while being transported from Africa to the Americas. It examines the thought process of the captain, the crew, the captives, and the legal system of England. D'Aguiar clearly illustrates the hell that was forced upon the blacks and how even the highest court system of the time saw nothing wrong with it. The whites were the ones who made the laws; the laws were meant to protect the whites. The high court had laws in place about proper procedures on these voyages, but the law wasn't meant to protect the blacks, or stock as they were referred to, just the well being of the white people involved. The common conception is that a courtroom is where the truth comes out and justice will be served. It is a safe haven for the innocent and a prison for the guilty. But when the hearing of the investors of the Zong vs. the insurers starts, Lord Mansfield states, "As you know, gentlemen, this is not a criminal trial. It is a hearing". No, this would never be a criminal trial. It wasn't illegal to murder black slaves if there was good enough reason. Blacks didn't have human rights like the whites did. Laws weren't created to protect the black man; they were there for the well being of the white person. Anyways, the black person was stock in the eyes of the law so the treatment of stock was the question at hand. "Which law did the captain break? None according to English statutes. What is being disputed here? Whether his actions were within the law that describes the treatment of slave stock." (p. 171) Whites made the laws, whites enforced them, whites benefited from them. ... ...mmunication there is still an underlying prejudice against the black person. Things haven't changed enough to say we are equal. Time is the main component in changing this. Something that has been rooted in white backgrounds and common laws for hundreds of years doesn't change in a few decades. Here at UW-La Crosse students are required to take a minority studies class and similar programs are underway at other colleges. Education is the first step to closing the gap. The second step is changing how one perceives another who is different from them. Will the world ever be able to do away with prejudice? Or is prejudice something that is like second nature. Everyone is entitled to their own thoughts, so wouldn't that entitle everyone to having a prejudice? Bibliography D'Aguiar, Fred. Feeding The Ghosts. A Novel. New York: The Ecco Press, HarperCollins, 1997.

Review of Related Literature on the Effect of Acid Using Vinegar as a Model on Mortality Rate of Freshwater Guppy Fishes Essay

Republic Act No. 9275 Philippine Clean Water Act of 2004 is an Act providing for a comprehensive water quality management and for other purposes. In Section 2 of this Act, it states that the State shall pursue a policy of economic growth in a manner consistent with the protection, preservation and revival of the quality of our fresh, brackish and marine waters. The State wants to manage and reduce the population of water resources of the country by promoting environmental strategies and use of appropriate economic instruments. The State recognizes that water quality is in the same level of concern of the quality of life. This Act also wants to promote commercial and industrial processes and products that will not harm the environment, which includes the living organisms in different ecosystems. Related Literature According to the special report, Acid Precipitation of Gene Likens from Cornell University during 1976, the acidity of rain and snow falling on parts of the U.S. and Europe has been rising—for reasons that are still not entirely clear and with consequences that have yet to be well evaluated. Acid precipitation has a long-term effect especially on the living organisms in many lakes and streams which sometimes causes extinction. Related Studies On the study of Schindler during 1988, Effects of Acid Rain on Freshwater Ecosystems, it was stated that there is an increase in number of areas most likely to be affected by acid. The study presented the biological damage caused by the acid rain, which includes the disappearance mostly of small fishes that are considered as food for larger predators which might cause these predators to starve and might result for another disappearance of fishes. Justification of Study Articles and past studies show that acid rain has a negative effect on living organisms from different ecosystems including freshwater. It was also mentioned in the study of Schindler in 1988 that the small fishes are most affected by the acidity of their environment. This study wants to know how affected these small fishes are thus, determining the mortality rate of guppies in environments with different levels of acidity

Ways

Mtunzini (Mm-tun-zee-nee) is a small coastal town that is situated almost exactly halfway along KwaZulu-Natal's coastline in South Africa approximately 140 km north of Durban. The name is an isiZulu word meaning place in the shade. After the breakup of the Zulu Kingdom after the Anglo-Zulu War, Sir Garnet Wolseley created 13 ‘kinglets' – with two strategically located as buffer zones between Port Natal and Zululand. One of these kinglets was John Dunn who used Mtunzini as his capital.Umlalazi Lagoon at dusk In 1948, 9 square kilometres of dune forests, lakes and lagoon at Mtunzini was proclaimed a nature reserve known as the Umlalazi Nature Reserve. This area falls under the protection of the Ezemvelo KZN Wildlife (previously known as Natal Parks Board). The Umlalazi Lagoon is a popular tourist attraction for watersports enthusiasts and fisherman alike. Recreational and commercial ski-boat boat fisherman also launch their boats in the lagoon and they then head for the In dianOcean via the mouth of the Umlalazi River. Mtunzini is a bird watchers paradise and is renowned as one of the few places where one of South Africa's rarest birds of prey, the Palm-nut Vulture, is found. These birds feed on the fruit of the Rafla Palm which produces its fruit once every twenty years before dying. Visitors can enjoy a walk through the lush vegetation at the Rafla Palm Monument, which features a raised boardwalk that meanders through to the magnificent palms.Mtunzini Beach Mtunzini boasts, among other attractions, pristine beaches, a 9 hole golf course at the Mtunzini Country Club, AA-Event and Guest House, numerous Bed-and-Breakfast establishments as well as a range of camping, caravanning and other self-contained holiday accommodations. Be warned, the beach is NOT protected by shark nets due to Mtunzini's proximity to a shark breeding ground populated by Zambezi Sharks as well as many others.

Second Great Awakening Essays

Second Great Awakening Essays Second Great Awakening Essay Second Great Awakening Essay hi chi connects the Second Great Awakening to the American Civil War. The final main reform ins paired by the Second Great Awakening was the Womens Rights movement. Womens paretic pupation in the revivals and the previously discussed reforms, eventually led to a reform Of the Eire own. During the Second Great Awakening women participation outnumbered mens two t o one. Finned and other revivalists spoke of empowerment and how one was in control of t heir own body and destiny. Women as a result were encouraged to participate in society. HTH is also the result of the social activism the Second Great Awakening caused. Both the Am Rican Temperance Society, one third to one half women, and the American Initials ere Society utilized substantial women participation. Women such as Angelina and Sarah Grime lectured men and women alike all over New England about the abolition cause in 1 837 , and when they were criticized for their gender, they responded by creating two essential word KS of feminism. These works were to explain the sisters desire for equal rights and are called Letters on the Condition of Women and the Equality of the Sexes and Letters to Catherine E. Beechen. Women became to become increasingly discontent lack of rights despite their participation in their community, encouraged by the Second Great Awakening, This movement t resulted in the Seneca Falls Convention, the first convention held concerning womens rights, in New York and set the quest for woman suffrage that lasted until 1 920 when the goal WA s accomplished. Women owe much of their success to the Second Great Awakening. On a political subject, the Second Great Awakening furthered Americas soups art Of Democracy. Since Finned repetitively and definitively established that humanity y is in control of he world and not a divine power.