Youth Wrestling and Nutrition free essay sample

Now, imagine two young and physically fit people wearing singlets. Both step up to the line and shake each other’s hand. In a brief moment of tension before battle, the referee blows the whistle and the first man shoots a double leg and takes an opponent down and earns two points. The sport of wrestling has been around for along time and dates back to 700 B. C. E. It was mostly used for military fighting and combatant techniques in ancient armies. In ancient Greece, it became a sport to where the opponent was forced to submit to tears. It was a great sport back in the day and it still is. American wrestling was introduced during the Second World War when a few soldiers witnessed the Japanese performing wrestling moves and it looked fun. So, the soldiers wanted to try it. Later, these soldiers showed other people how to do it. We will write a custom essay sample on Youth Wrestling and Nutrition or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page Soon, a wrestling sensation spread. These days, wrestling is a competition between two people for six minutes and it takes a great deal of effort and strength and determination to conquer the opponent. Wrestling is a sport of skill, strength, and endurance. Picture it like this. A small skinny person in the 138 weight class could end up wrestling a muscled out bodybuilder of the same weight. However, if the smaller kid uses the proper technique, then there is no need to worry about the strength of the other wrestler. The technique of a wrestler is the most important factor in a wrestling match. Good techniques provide a better chance of winning while a poor technique can cause one to be lying flat on there back. Coaches have to show wrestlers the proper techniques of wrestling (â€Å"leaguelineup. com† 1). Most coaches start off with showing the athlete how to shoot. The fundamental importance in a wrestling match is shooting on an opponent. It forces one participant to the mat and the other one get the points. Now, there several different ways to shoot. There is a single leg, a double leg, run the pike (variation of a single leg), single leg then the player switches to a double. † Those are some of the basic moves for a wrestler and the wrestler needs to practice these moves until it becomes a muscle memory and it can be done fluidly and easily. The next part is the defensive part of the shot it is a sprawl. The sprawl lets the wrestler put weight on the person shooting, forcing the head into the mat, stopping the person from shooting, and then getting behind the person and get two points† (â€Å"leaguelineup. com† 1). Next, the wrestler must learn how to ride a person. Once the opponent is down, one thing to make sure of is to not ride the hips because it is considered stalling (doing nothing for a period of time). â€Å"So, the wrestler learns how to drive the opponent forward while chopping the arm from underneath to cause the other player to lay flat. There are a few other ways that this move can be done. However, this is the basic move. So, after being broken down, begin to pin the opponent. â€Å"There are a few basic ways to achieve a half nelson, an arm bar, a double arm bar, a barb wire, then an assortment of different cradle options. However, all of these moves work on pinning the other guy to the mat. † In wrestling, a pin is counted when both shoulder blades touch the mat or a win is decided by either a pin or if a wrestler gets fifteen points ahead of the opponent. One can also win with more points than the opponent at the end of the six minute match. If both participants have the same number of points at the end, then overtime is called and the winner is decided by whoever gets the first take down of the overtime. â€Å"Those are the basics of the wrestling plethora of moves that you can do, but for most moves, you need to be in shape and healthy which makes nutrition one of the most important factors of a wrestler† (â€Å"leaguelineup. com† 2). A wrestler needs to be in top physical condition and this includes nutrition, good nutrition is one of the key factors to a wrestler’s performance. A healthy, fit wrestler has greater strength, endurance, and focus on the mat which will help increase the chances of winning the match. However, there is one small thing wrestlers need to do and that is to watch out for the weight cutting methods such as starvation, dehydration, and wearing a sauna suit. â€Å"These methods are extremely dangerous and could actually cripple a wrestler’s performance. † Sure weight will be lost, but at what cost. If anything, it is better to move up a weight class rather than go down (â€Å"usawct. org/coachescorner/diet† 1). A wrestler needs the nutrients and other minerals the body requires to stay healthy. These include carbohydrates, proteins, dairy, fruits and vegetables, vitamins, and minerals. These build muscle mass and keep the body healthy. During the week one can start off the day with a breakfast of whole grain wheat toast with milk or orange juice or a fruit. For a snack in between breakfast and lunch, eat an apple. Then for lunch eat a salad or a turkey sandwich. Stay away from the school lunches for most have unneeded fats and carbohydrates. For dinner, make it small portions. Portions should be no bigger than a fist and try to get all the food groups in there. Mostly, a wrestler’s meal should consist of carbohydrates and proteins to help muscles recover. Of course, all the other stuff is needed, but these two things are the most important factors besides water. Now, not everybody needs the huge build like some of the people sometimes see (â€Å"usawct. org/coachescorner/diet† 2). A wrestler can achieve strength by doing light to heavy, quick weightlifting regiments to get the body accustomed to what will be experienced on the mat. Depending on the weight class for example, a 138 pounder should start weight lifting at say thirty-five pound dumbbells or kettle balls. If squatting, one should be able to do at least the wrestlers body weight. â€Å"To be sure that they don’t over work themselves, they should do about 3 sets of 8 repetitions or 2 sets of 12 repetitions and only do about two or three exercises a day and give that muscle group at least 48 hours to repair itself. † So that means do not do the same exercises the next day. Do a different muscle group because during a work out these exercises are tearing the tiny little muscle fibers in the tissue and when they repair, they don’t stay the same size neither will they keep the same strength. † So as they work out one will notice within a week or so, that they’ll be able to do more and more. One will become stronger and faster and more fit to wrestle and the movement from light to heavy weights while doing quick repetitions, will help build muscle endurance so the body does not tire out in the middle of the match (â€Å"bodybuilding. om† 2). The conclusion of all of this would make a strong, fit, dedicated wrestler as long as one eats and drinks right and knows enough technique and skill to out match the opponent. There is nothing more exciting than watching a wrestling match between two, well trained, dedicated, and physically fit combatants. It is a sport of strength, will, and endurance. The Greeks would probably not enjoy this sport done the American way, but in any case, it continues to be a popular sport enjoyed by teens, adults, and audiences.

About Daniel Libeskind, New Yorks Master Planner

About Daniel Libeskind, New York's Master Planner Architects design more than buildings. An architects job is to design space, including the spaces around buildings and in cities. After the terrorist attacks of September 11, 2001, many architects submitted plans for reconstruction on Ground Zero in New York City. After heated discussion, judges selected the proposal submitted by Daniel Libeskinds firm, Studio Libeskind. Background: Born: May 12, 1946 in Là ³d’z, Poland Early Life: Daniel Libeskinds parents survived the Holocaust and met while in exile. As a child growing up in Poland, Daniel became a gifted player of the accordionan instrument his parents had chosen because it was small enough to fit in their apartment. The family moved to Tel Aviv, Israel when Daniel was 11. He began playing piano and in 1959 won an America-Israel Cultural Foundation scholarship. The award made it possible for the family to move to the USA. Living with his family in a small apartment in the Bronx borough of New York City, Daniel continued to study music. He didnt want to become a performer, however, so he enrolled in Bronx High School of Science. In 1965, Daniel Libeskind became a naturalized citizen of the USA and decided to study architecture in college. Married: Nina Lewis, 1969 Education: 1970: Architecture degree, Cooper Union for the Advancement of Science and Art, NYC1972: Postgraduate degree, History and Theory of Architecture, Essex University, England Professional: 1970s: Various architectural firms, including Richard Meier, and various teaching appointments1978-1985: Head of School of Architecture, Cranbrook Academy of Art, Bloomfield Hills, Michigan1985: Founded Architecture Intermundium, Milan, Italy1989: Established Studio Daniel Libeskind, Berlin, Germany, with Nina Libeskind Selected Buildings Structures: 1989-1999: Jewish Museum, Berlin, Germany2001: Serpentine Gallery Pavilion, Kensington Gardens, London2002 (selected in February 2003): Ground Zero Master Plan2003: Studio Weil, Mallorca, Spain2005: The Wohl Centre, Ramat-Gan, Israel1998-2008: Contemporary Jewish Museum, San Francisco, CA2000-2006: Frederic C. Hamilton Building at the Denver Art Museum, Denver, CO2007: The Michael Lee-Chin Crystal at the Royal Ontario Museum (ROM), Toronto, Canada2008: Westside Shopping and Leisure Centre, Bern, Switzerland2008: The Ascent at Roeblings Bridge, Covington, Kentucky (near Cincinnati, Ohio)2009: The Villa, Libeskind Signature Series, prefabricated house available Worldwide2009: Crystals at CityCenter, Las Vegas , Nevada2010: 18.36.54 House, Connecticut2010: The Run Run Shaw Creative Media Centre, Hong Kong, China2010: Bord Gis Energy Theatre and Grand Canal Commercial Development, Dublin, Ireland2011: Reflections at Keppel Bay, Keppel Bay, Singapore2011: CABINN Metro Hotel, Copenhagen, D enmark 2013: Haeundae Udong Hyundai IPark, Busan, South Korea2014: Ohio Statehouse Holocaust Memorial, Columbus, Ohio2014: Beyond the Wall, Almeria, Spain2015: Sapphire, Berlin, Germany2015: Centre De Congrà ¨s Mons, Mons, Belgium2015: Zhang ZhiDong And Modern Industrial Museum, Wuhan, China2015: CityLife Master Plan, Central Tower C, and Residences, Milan, Italy Winning the Competition: The NY World Trade Center: Libeskinds original plan called for a 1,776-foot (541m) spindle-shaped Freedom Tower with 7.5 million square feet of office space and room for indoor gardens above the 70th floor. At the center of the World Trade Center complex, a 70-foot pit would expose the concrete foundation walls of the former Twin Tower buildings. During the years that followed, Daniel Libeskinds plan underwent many changes. His dream of a Vertical World Gardens skyscraper became one of the buildings you wont see at Ground Zero. Another architect, David Childs, became the lead designer for Freedom Tower, which was later renamed 1 World Trade Center. Daniel Libeskind became the Master Planner for the entire World Trade Center complex, coordinating the overall design and reconstruction. See pictures: What Happened to the 2002 Plan for Ground Zero?One WTC, Evolution of Design, 2002 to 2014 In 2012 the American Institute of Architects (AIA) honored Libeskind with a Gold Medallion for his contributions as an Architect of Healing. In the Words of Daniel Libeskind: But to create a space that never existed is what interests me; to create something that has never been, a space that we have never entered except in our minds and our spirits. And I think thats really what architecture is based on. Architecture is not based on concrete and steel and the elements of the soil. Its based on wonder. And that wonder is really what has created the greatest cities, the greatest spaces that we have had. And I think that is indeed what architecture is. It is a story.- TED2009 But when I stopped teaching I realized you have a captive audience in an institution. People are stuck listening to you. It is easy to stand up and talk to students at Harvard, but try doing it in the marketplace. If you only speak to people who understand you, you get nowhere, you learn nothing.- 2003, The New Yorker There is no reason that architecture should shy away and present this illusory world of the simple. It is complex. Space is complex. Space is something that folds out of itself into completely new worlds. And as wondrous as it is, it cannot be reduced to a kind of simplification that we have often come to be admired.- TED2009 More About Daniel Libeskind: Counterpoint: Daniel Libeskind in Conversation with Paul Goldberger, Monacelli Press, 2008Breaking Ground: An Immigrants Journey from Poland to Ground Zero by Daniel Libeskind Sources: 17 words of architectural inspiration, TED Talk, February 2009; Daniel Libeskind: Architect at Ground Zero by Stanley Meisler, Smithsonian Magazine, March 2003; Urban Warriors by Paul Goldberger, The New Yorker,, September 15, 2003 [accessed August 22, 2015]

Case study dealing with parents Essay Example | Topics and Well Written Essays - 250 words

Case study dealing with parents - Essay Example Sandy should make out time and discuss the role of the parents in getting their child learn English. She should also discuss the effects of their parenting style on the success of their child to learn the English language through socialization (Darling & Steinberg, 1993). According to Minke and Anderson (2005), it is important for professionals to include families in planning, implementing and evaluating support programs in schools. From the look of things, the Japanese parents were interested in getting their son learn the English language in the shortest time possible. However, they did not understand that their involvement in the support program was very tremendous. They also did not provide background information about their child. This made it difficult for Sandy to follow the request of the parents within the provided time period. The school can deliver the requests of the parents by improving on how they interact with parents. This includes having adequate discussions with parents about their children and what the parents needed the teachers to do. The teachers could then propose the necessary programs to parents and involve them in outlining the course of action (Darling & Steinberg, 1993). This ensures that parents get involved designing appropriate support programs for their children hence they get the best

Morpholins anti-cancer drugs Dissertation Example | Topics and Well Written Essays - 3250 words

Morpholins anti-cancer drugs - Dissertation Example They produced morpoline derivatives which included both esters and N-alkyl derivatives. The target compounds were characterised using H1, C13NMR, IR and were studied as inhibitors of ÃŽ ²-D-galactosidase extracted from Bovine kidney. The activity of this enzyme is associated with a variety of diseases therefore the produced morpholine derivatives have potential medical applications. p-nitrophenyl-ÃŽ ²Ã¢â‚¬â€œD-galactopyranoside was chosen as a substrate for the enzymatic cleavage of the carbon-oxygen bond which is catalysed by ÃŽ ²-D-galactosidase. During the process p-nitrophenol was released in the environment, the compound had the ability to absorb light in the visible region of the electromagnetic spectrum, therefore, it was possible to estimate its quantity using the Beer–Lambert–Bouguer law. By measuring the quantity of the produced p-nitrophenol at set time intervals conclusions were drawn regarding the reaction speed and, consequently, about inhibition propertie s of the studied morpoline derivative. The reaction was followed by Michaelis-Menten kinetics, therefore reaction speed was calculated using the most linear fragment in the dependence between absorption and time. Because the enzyme was denatured, its inhibitory properties were not tested and are a subject for future work. Cell’s surface is composed of lipids, carbohydrates and proteins. Compared to other surface molecules glycolipids and glycoproteins are the longest. For this reason they are often take part in interactions with substrates or other cells, consequently carbohydrates are of paramount importance in cellular interactions and disease processes such as cancer, infections or inflammations. ... The compound can covalently attach to serine or threonine. The process leads to formation of clusters in which one monosaccharide is linked to one amino acid. The produced clusters are often the ideal targets for antitumor antibodies. Such antibodies can be generated by glycopeptides linked to clustered sialyted epitopes. The effectiveness of which is usually higher then single sialyted epitopes (Butters, et al., 2003). Glycosidases classification is based on the similarities in the sequence of their amino acids (Table 1) (Henrissat and Bairoch, 1993; Henrissat, 1991). Enzymes within the same group share the same structural features and perform their functions using the same mechanism (Rye and Withers, 2000) Usually, there are two mechanisms employed by enzymes to cleave glycosidic bonds. As a result, a free hydroxyl group is formed with retention or inversion of configuration (Scheme 1)(Sinnott, 1990; Zechel and Withers, 2000; Vasella, et al., 2002). In the mechanism (a) glycosidase s cleave the required bonds using asparagine and glutamine 6A apart from each other. One carboxylic group is deprotonated an acts as a base by abstracting a proton from water during the formation of the intermediate (Withers and Umezawa, 2001; Davies, et al., 2005; Hoj, et al., 1992). Table 1. Type of carbohydrate-active enzyme and its function Carbohydrate-active enzyme Abbreviation Function Carbohydrate Esterases CE Carbohydrate esters hydrolysis Polysaccharide Lyases PL Non-hydrolytic cleavage of glycoside bonds GlycosylTransferases GT Glycosidic bonds formation Glycoside Hydrolases GH Glycosidic bonds rearrangement or hydrolysis The remaining carboxylic group protonates the oxygen atom from the anomeric centre and assists in its removal. Both bond formation and

M2A2 Essay Example | Topics and Well Written Essays - 500 words

M2A2 - Essay Example These individuals may lead the followers to failure by spending too much time socializing and having fun not to mention letting the followers to be extremely free of responsibilities. Such people cannot be effective leaders if results are expected to be seen within certain duration. Attitudes may hinder or enforce the leadership qualities of an individual. This is because attitudes are connected with emotions which make an individual behave in a rational or irrational manner when communicating with others. Leaders with attitude problems (negative attitude) tend to be very pessimist even to the work of his or her followers. This may demoralize and demotivate them hence leading to the failure of a course or organization (Ricketts and Ricketts 2010). Values are acquired through socialization process by different socialization agents like family, school and media. The personal values of an individual may affect their leadership. If for example a person has staunch moral values, he or she will uphold principles of integrity and democracy not to mention good governance hence becoming a good leader. One of the strategies is to have emotional and social intelligence which will ensure the leader understands their emotions and attitudes and that of others and will therefore be considerate and understanding to the followers hence overcoming weaknesses. The other is to have therapies to manage the personal weaknesses the leader may have that hinder him or her from becoming an effective and efficient leader. Situational variables reflect communication of different types of leaders to their followers. It involves defining the tasks accurately and clearly and also understanding the physical and social surroundings that may hinder him or her to be a good leader. These factors may help an individual develop his or her leadership in a way that both the followers and other stakeholders will be supportive to the organization’s endeavors. The organizational variables that may

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. 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Strong Chinese Women in Film Essay -- Character Analysis

1. Introduction In Confucian thought, women had their purpose beside their men or within their households as mothers. However, the legend of Hua Mulan precedes Confucius. Mulan’s story had inspired early Chinese Feminists such as Qiu Ji to go against the society built to keep her space as a woman separate from the rest of the world. In modern times, Fa Mulan (from Disney) added more diversity to the usual Disney Princesses and gave westerners an image of Chinese culture. The Disney film about Fa Mulan and the live action film about Hua Mulan by Jingle Ma ultimately chronicle the journey of Mulan and her service in the military but the films will have significant differences because of the different perspectives telling the stories. The changes of the female roles in China in the 21st century have their starting point with the story of Mulan because of her positive role in female identity later on. How Mulan changed the stigma about women over time may not have helped. Although Mulan is a legend, legends tend to shape some fields of thought in society. Changes in female structures in China took many centuries but I believe Mulan’s presence had an irreplaceable impact on the women in Chinese society. I define Confucianism in the female role as follows: A woman’s duties pertain to her husband, the parents of her husband and the children birthed between she and her husband. A woman’s duties to her husband include but are not limited to, keeping him happy and full with good meals. Her duties to her husband’s family includes, but are not limited to, keeping his parents happy and adjusting to the rules of her governing mother-in-law and providing grandparents with grandchildren. A woman’s duties to birth children include, but are not... ... to the Imperial City to warm her old friends of the imminent attack on the emperor, everyone ignored her. Hua Mulan did not face this same problem in the live action movie. When her comrades discovered her, they decided to keep their discovery to themselves instead of sending Mulan to her death. The only reason Fa Mulan remained alive in the movie was her heroic actions before her superior discovered her. Works Cited 5. Confucianism Since the core of Confucianism is the belief 6. Conclusion References: 1. http://ww.chinapage.com/mulan.html 2. â€Å"Ode To Mulan† http://www.yellowbridge.com/onlinelit/mulan.php 3. â€Å"Mulan in Legends† http://www.ourorient.com/mulan-in-legends.htm 4. DVD Disney’s Mulan 5. DVD Jingle Ma’s Mulan 6. Lan, Fen. "The Female Individual and the Empire." Duke University. http://www.jstor.org/stable/pdfplus/4125407.pdf.