Du, De La, Des Expressing Quantities in French

Expressing quantities is quite an important part of daily conversation. In French, the key to understanding how to express quantity is a question of the specification of quantity: a  precise quantity, or a vague one. Most of the times, you wont be able to translate word-for-word from English, so you need to understand the logic to choosing the correct word in French. Quantities in French There are several ways to express quantities in French:Numbers:  The most precise way to express a quantityExpressions of quantity:  A little bit of, or many, or half; these can be more or less preciseAn  adjective of quantity: Aucun (none) or plusieurs (several)An indefinite article: A, anA  partitive article: Some, any Unspecified Singular Quantity: Du, de La, de L’– Unspecified quantities represent the notion of â€Å"some† in English, but we don’t always use the word â€Å"some.† When you are talking about a portion of one item (food, like some bread), or something that cannot be quantified (quality, like some patience), use what the French call a partitive article. du ( masculine word)de la ( feminine word)de l’ – (followed by a vowel) Examples: Je voudrais de l’eau, s’il vous plait  (some water—maybe a glass, or maybe a bottle)Le professeur a de la patience  (patience—you are not saying how much patience the teacher has, just that he/she has some)Voici du gà ¢teau  (some of the cake; not the whole cake) In these examples, some applies to a singular item. Here is some cake, rather than some cakes, which we will study below. Here, we are talking about a portion of one item—a portion that is vague, not specific. The articles du, de la, and de l–  are called partitive articles in French. It is important to note that these articles are often used after the verbs vouloir (â€Å"Je voudrais des chaussures noires†) or avoir (â€Å"J’ai des chats†) and with food (we use these all the time with food, so its a good topic for practice). More Than One, but Unspecified Plural Quantity: Des To describe an unspecified plural quantity, use â€Å"des† (both feminine and masculine), which  tells you there is more than one item, but it is a vague plural quantity (it could be 2, could be 10,000 or more). This â€Å"des† usually applies to whole items, that you could count, but decided not to. Examples: J’ai des Euros  (more than one, but I am not telling exactly how many)Je vais acheter des pommes  (I’m going to buy apples. In English, we’d probably won’t use any words before apples. Maybe some, but in French, you need to use â€Å"des†)Elle a des amis formidables (she has [some] great friends) In English, the word â€Å"some† is used for unspecified quantity (I would like some milk) but also as a derogative adjective (he went home with some girl). In French, you would never say â€Å"il est rentrà © chez lui avec de la fille,† as he didn’t go home with an unspecified quantity of a girl. So be careful, word-for-word translation doesn’t always work! The same thing goes for the example, â€Å"elle a des amis formidables.† In English, if you say â€Å"she has some great friends,† you’d be strongly implying that her other friends are not so great. In French, we use an article where, in English, you’d probably use nothing: â€Å"she has great friends†.   Some food items are usually referred to as singular, although they are really plural. Like rice. There are many grains of rice, but it’s rare that you are counting them one by one. Thus, rice is considered a single ingredient, expressed using the singular masculine, â€Å"le riz†. If you need to count each grain, then you’d use the expression, â€Å"grain de riz† – Il y a 3 grains de riz sur la table (there are 3 grains of rice on the table). But, more often, you’d say something like â€Å"j’achà ¨te du riz† ( I am buying [some] rice).

The Wife of Bath Essay - 947 Words

The Wife of Bath The Wife of Bath, one of the many characters in Chaucers The Canterbury Tales, is a feminist of the fourteenth century. Chaucer, in the General Prologue, describes her as promiscuous. The Wyf confirms this claim in the prologue to her tale, the longest in the book. An analysis of the General Prologue and the Wyfs Prologue reveals a direct relationship between the Wyf of Bathe and the characters in her tale, such as the knight, queen, and ugly woman. There is a direct correlation between the physical characteristics of the Wyf of Bathe and the thematic structure of her tale. The way Chaucer describes her, gives the reader an inside view to the Wyf of Bathe. In the General Prologue, for example, Chaucer†¦show more content†¦The Wyf is in a similar situation. Because she depends on men, living without one has the same effect on her, as losing her life. She goes on pilgrimages to meet men: thryes hadde she been at Jerusalem; She hadde passed many a strange streem; At Rome she hadde been, and at Boloigne, In Galice at Seint Jame, and at Coloigne(465) She is constantly searching, even whilst she is married. The knight travels from house to house in search of the answer to the queens question. When he doesnt find the answer on his own, he must get help from an ugly woman, in return for husband in marriage. She forces him to settle for a woman he thinks to be loothly and so old also(WT 244). It is only after they are married and he gives in to the ugly woman that she becomes beautiful and they live happily ever after. The Wyf marries men like the knight. She will no lenger in the bed abyde,/ If that I felte his arm over my syde,/ Til he had maad his raunson unto me(WP 410). He must give in to her, leaving her in complete control of the marriage, before she makes him happy. Furthermore, the Wyf of Bathes aspirations parallel those of the queen in her tale. The queen is what the Wyf aspires to be. The queen is nobility, and the Wyf, although she can never be of noble blood, tries to make up for it with her appearance. She dresses up in new, fine clothing to appear rich, and noble.Show MoreRelatedThe Wife of Bath1145 Words   |  5 Pagesone of them is the story of the Wife of Bath, whose real name is Alisoun. From her appearance and behavior, to her political and religious views, there is much to tell about the Wife of Bath, for her prologue and tale are quite long. The Wife of Bath is a very interesting character. In addition to Alisoun as a person, her story is fascinating as well, with a surprising and compelling end to the story. (SparkNotes Editors) According to the story, the Wife of Bath has a very distinct appearanceRead MoreThe Wife Of Bath1531 Words   |  7 PagesHeaven knows whenever he wanted it- my belle chose-, thought he had beaten me in every bone†¦Ã¢â‚¬ (272) Even though her final husband had beaten her, because he was good in bed with her she felt she loved him the best of them all (272). Clearly, The Wife of Bath valued three things in her marriages, sex, power, and money. In her tale we find that power is an important role to women in marriage. A knight, after raping a women is spared by a queen (282) but in order to save his life, he has one year (283)Read MoreThe Wife of Bath1326 Words   |  6 Pagestheir journey. One of the travellers, the Wife of Bath shares her views on social relationships between men and women. The fourteenth century is viewed as having a patriarchal dominated society. However, the Wife of Bath, Alisoun, is a strong believer in female maistrie, control in the marriage. She b elieves in female supremacy over husbands in marriage, and does not feel they can be equal partners in the relationship. Through her prologue and tale the wife justifies the actions she and other womenRead MoreThe Wife Of Bath, By Chaucer Essay970 Words   |  4 Pagesto explore the Wife of Bath, her character, appearance, and tale. For the purpose of establishing a correlation between; the perceptions of the other pilgrims, the Wife’s apparent nature, and the tone of her tale. Slade suggest that Chaucer intended the Wife as an ironic character (247). A perspective that is supported by Chaucer’s treatment of the Wife in her description and prologue. The Wife, unlike the other pilgrims who are identified by their occupations, is identified as a wife. Regardless ofRead MoreThe Wife of Bath, The Wife of Bath Prologue, and The General Prologue981 Words   |  4 PagesThe Wife of Bath, The Wife of Bath Prologue, and The General Prologue These selections from The Canterbury Tales best exemplify the ideals and traits of women (as portrayed by Chaucer). In, The Wife of Bath Prologue, the narrator brags of her sexual exploits as well as her prowess of controlling men. The narrator is quite forthright in her enjoyment of this manipulation; she comments on her technique of lying and predomination of men. The General Prologue further servesRead MoreAnalysis Of The Wife Of Bath 1660 Words   |  7 PagesThe Canterbury Fails: An Analysis of Misogyny in the Wife of Bath’s Tale At first glance, you wouldn’t think that the Wife of Bath’s tale is anything other than feminist. She is, undeniably, the only non-religious female character in The Canterbury Tales and therefore is the only character who is approached from a point of view that was generally uncommon. We don’t have many— or even any, as far as I’m aware— pieces of medieval literature written by or for women or with a main female protagonistRead MoreThe Wife of Bath Essay715 Words   |  3 PagesWife of Bath vs. Lady Gaga Geoffrey Chaucers, Wife of Bath, character in Canterbury Tales can be compared with todays modern pop icon Lady Gaga. Both woman share many similar qualities regarding their personality types and behavior. From the Fifteenth century to the Twenty- First, these women symbolize feminism and contradiction of societal norms. This essay will discuss the similarities and differences between Chaucers fictional character, the Wife of Bath, and Lady Gaga, one of this century’sRead MoreThe Wife of Bath Essay940 Words   |  4 Pagescorrupt and flat out crazy characters. However, The Wife of Bath is one character that stands out the most. She is a strong, sexual being who does not care about obeying the rules. The Wife of Bath speaks highly of herself when it comes to pleasing her man sexually and does not believe that when one marriage ends that is it; she believes that more opportunities open. She marries five men, four of them for money and one for love. The Wife of Bath is not perf ect in her tale but she keeps her audienceRead MoreEssay on The Wife of Bath1031 Words   |  5 PagesThe Wife of Bath Historical Background One of the most memorable pilgrims of The Canterbury Tales, as well as one of the most memorable women in literature, is the Wife of Bath. She is a lusty and domineering woman who is proud of and outspoken about her sexuality and believes that a woman should have sovereignty in a marriage (Norton 80). She is also extremely blunt and outspoken about her ideas and beliefs. Despite being a woman of the fourteenth century, her ideas, beliefs, and behaviorRead MoreThe Moral Of Wife Of Bath990 Words   |  4 Pagesequal rights to men, but have yet to establish a non-submissive relationship with their male partners. The moral of Wife of Bath is the desire women have to have power over their husband and how this dominance is beneficial for them and through the course of the tale, the speaker makes an effort to express her views of control in a happy marriage. The moral of Wife of Bath is that happiness in a relationship is when a woman is able to have control over her husband against a backdrop of

Automatic Sentence Generator free essay sample

Not only will we be able to automatically generate sentences associated with the theme being modeled, but we will also be able to help recognize phrases and sentences. In other words, this is a module which could be part of an automatic speech recognition system, so that proposed recognized word sequences can be validated according to acceptable contexts. The system is adaptive and incremental, since models can be modified with additional training sentences, which would expand a previously established capacity. Key words: corpus, vocabulary, training, recognition, recognizer, generator, histories, context, decoder. . Introduction. The growing, unstoppable development of very high speed information processing computers with tremendous main memory capacity which we see today leads us to think that it will be possible to design and construct automatic speech recognition systems which can detect and code all the grammatical components of a training corpus. As part of our effort to make a contribution to the fascinating world of Automatic Speech Recognition, we have developed a system composed of a set of computer programs. We have observed that on the basis of a model of a small corpus made up of sentences in a particular context, we can automatically generate a great quantity of grammatically correct sentences with this context. Also, our system can effect a linguistic discrimination to the point of rejecting, as out of context or grammatically incorrect, those word sequences with words or word histories not registered in its memory. We believe that a system that processes information in the way we describe in this paper can work successfully in recognition tasks of a variety of context whose vocabulary size extends to thousands of words. . Terminology. Training corpus. The set of sentences and paragraphs used to construct the context model used for the generation and recognition of phrases. Vocabulary. The set of distinct words found in the training corpus. Training. Process which results in the creation of context models. Histories. Sets of words that appear contiguously in the training corpus, [1] . For example, if the following sentence is part of the training corpus â€Å"there are three reasons which seem to be the origin of this fact†, then a history of two words could be â€Å"the origin†, and a history of three words would be â€Å"the origin of†. Context. Knowledge area to which belong the sentences and paragraphs of the training corpus. Recognition. The processing of a word sequence and deciding whether it is a valid sentence with regards to the grammatical rules which have been established in the context model. Sentence generation. The process which creates a sentence based on the context model. Grammatically valid sentence. A sentence which has a structure which follows the grammatical rules that have been detected in the training corpus. Sentence hypothesis. The set of possible sentences corresponding to a word sequence that is to be recognized or generated. 3. Context model generator. In figure 1, we show the principal elements of the system, the inputs, the outputs, and a graphical indication of how the elements interact. A word sequence Linguistic decoder Recognized sentence Training corpus Context model Vocabulary Generator of sentences Genered sentence Figure 1. System structure. To create a model, we begin with a grammatically correct set of sentences and paragraphs pertaining to the context which we wish to model. We model the context, even though we believe that if we feed the model incrementally, we could eventually obtain a good model of the language to which the training corpus belongs. The training or modeling of the context consists basically of the search, coding and saving of the occurrence of contiguous word histories corresponding to the sentences and paragraphs of the training corpus. We create coded blocks of word histories. The words in the histories are coded by way of whole numbers. In figure 2, we give an idea of how we code the histories in the training corpus. The first word that appears in the first training corpus (recall that we can work incrementally with various corpus) is assigned the number 1, then, the next different word is assigned the number 2, and so on. The nth different word is assigned the number n. text to whole numbers histories of two words the origin . . . . . . . . the origin of . . . . . . . 9 10 .. .. .. .. .. .. .. .. 9 10 11 .. .. .. .. .. .. .. .. text to whole numbers histories of three words Figure 2: Examples of histories blocks taken from the training corpus. The following blocks are formed: A block which has a coded list of the words which begin sentences in the corpus; this block is used by the generator of sentences and is called Block 1. A block which has a list of histories of 2 words. This block may be used by the generator of sentences as well as by the linguistic decoder. We call this Block 2. A block which has a list of histories of 3 words; like Block 2, it may be used by the generator of sentences as well as by the linguistic decoder. This block contains triplets of words in the training corpus. We will call it Block 3. A block which has a list of histories of 3 words like Block 3, but with the difference that these histories only include the ends of sentences and paragraphs of the corpus. This will allow the sentence generator to know how to finish sentences. This is Block 4. The coding of the training corpus in Blocks 1, 2, 3 and 4, facilitates the creation of sentence hypotheses for linguistic recognition, for sentence generation, and to make data processing more agile. Once these blocks have been created and coded, some histories are discarded to reduce some redundancy in the coding, and to accelerate the programs that recognize sentences and generate sentences. History elimination is somewhat arbitrary; for example, some experiments were performed, on the one hand, eliminating the histories of two words of low frequency, and on the other, saving the histories of 3 words which began with these two words. Suppose the combination â€Å"subject to† appears only once, and the sequence â€Å"subject to conditions† also appears; then the 2 word combination would be eliminated. Whereas if the combination â€Å"the history† appears seven times, then it would be saved. Working in this fashion, we hope to assure that even those histories that have low frequency will still be accounted for when we do recognition and generation. It is clear that if we exclude many histories of 3 words, overall system performance will degrade. Another option is to add more sentences to the corpus with the sequences that are being eliminated, which would require additional training. This is the one we prefer, since the model which is created in this fashion is more complete, adaptive and incremental, and allows us to repeat the procedure with another training set, without losing information obtained during the previous passes. So that in an incremental fashion, we can enrich not only the model vocabulary, but also increase its capacity to generate sentences and perform recognition. The process of retraining or adaptation is done after we have created a model based on a corpus which we will call Corpus 1. We can select other sentences of the same context, different from the original, and thus make Corpus 2. Using this new corpus, we code all its histories, and add the frequency of appearance to those that appeared in Corpus 1, thus creating an accumulated frequency for these histories. We see that we have thus created an extension to the model created with Corpus 1, to which we are adding new histories and even new words to its vocabulary. This process can be repeated as many times as considered necessary for our applications. Nevertheless, finally, we only store in Blocks 1, 2, 3 and 4 the histories of two words that have the most frequency, plus the histories of three words which initially contain those two word sequences that have been eliminated. 4. Sentence generator. Based on the model described previously, the system is capable of randomly producing sentences with the given context. The algorithm for generating sentences is as follows: 1. A word from the Block 1 list is randomly selected, say w1, and is shown on the system monitor. For example, the word â€Å"This† is selected from Block 1. 2. From Block 3, those histories which begin with the word w1 are grouped into a new block, the subblock 31. To continue the previous example, we would then have â€Å"This specification should†, â€Å"This declarative sentence†, â€Å"This sentence belongs†, â€Å"This distinction allows†, â€Å"This plays a†, etc. 3. If the subblock 31 is not empty, then one of the alternatives is randomly selected, and the next two words which follow w1 are shown on the system monitor. For example if the history â€Å"This sentence belongs† was selected, then â€Å"sentence belongs† appears on the screen. 4. We then proceed with a search for histories in Block 2. The ones of interest in this block are those who have as a first word the last word which appears in the history selected from subblock 31, when this is not empty, say histories that begin with w3. If subblock 31 is empty, then the histories of interest are those that have as an initial word w1. In this way, a new subblock, subblock 21, is formed. Continuing our example, we might have only â€Å"belongs to†, and subblock 21 would have only one entry. 5. If subblock 21 is not empty, one of its entries is selected randomly, and the second word of this history is shown. Following the previous example, the word â€Å"to† appears. So far we have the phrase â€Å"This sentence belongs to†. 6. The process continues with a search in Block 4 which contains sentence ending histories. The ones of interest would be those that begin with the last word in the last history previously selected. We then form subblock 41. For our example, the histories of interest would be those that begin with â€Å"to†. If no histories are found that begin with this word, then subblock 41 would be empty. 7. If subblock 41 is not empty, then one of its entries is randomly selected, and the last two words of the sentence are shown. In this way, a sentence generation has been completed. . If subblock 41 is empty, then w1 takes the code associated with the last word. In this example, the coded associated with to. 9. Go to step 2. In our example, after going to step 2 and when the process is completed, we got the sentence this sentence belongs to a syllabic rhythm. The majority of the sentences that are produced in this fashion are not to be found in the training corpus, but rather have been formed from an appropriate linking of coded histories. The sentence that we formed in our example does not exist in our corpus, but others do that we indicate as follows : a. â€Å"This type of sentence belongs to a neutral annunciation without expressive aspects and specials appellative†. b. â€Å"It belongs to the last syllable bearing of lexical accent in the melodic group†. c. â€Å"This declarative sentence is formed for three tonal units†. In summary, the sentence is generated through a successive search of words in the histories of Blocks 3, 2 and 4. The process ends when at least one history from Block 4 is found or when no history exists in any of these 3 blocks to continue the sequence. This may be the case when a history has been selected which in the corpus is at the end of a sentence, and is not ppropriate for forming a continuing sequence. For this work, the sentence generator descr ibed was designed and implemented as a computer program, in order to have an idea of the sequence of words that could be recognized by the linguistic decoder, which we would develop later on and which we will describe in the next section. At this moment, we can suppose that the context model is a network of coded histories that contain the sentences that can be recognized by the linguistic decoder. The utility of the sentence generator in this work was foreseen only to show the sentences present in the model and that therefore can be recognized. . Linguistic recognizer or decoder. The part of a speech recognizer that converts acoustic data from a pronunciation to a sequence of linguistic symbols (as for example, a sequence of phonemes, a sequence of words, etc. ) is called an Acoustic Decoder, while the Linguistic Decoder is that part of the speech recognizer which determines if that sequence of symbols corresponds to a valid sentence in the language. [2] As can be surmised from Fi gure 1, in this work we only develop a linguistic decoder so that any tests suppose the existence of a sequence of words as generated by an acoustic decoder. The procedure by which the system can recognize a sequence of words (w1, w2, , wn) [2] as grammatically correct based on a context model, is described as follows: 1. Receive the first word of the sequence, w1. 2. Determine if w1 is part of the vocabulary. If w1 is not part of the vocabulary, then it is rejected, as is also the sequence, since it is out of context. If it is part of the vocabulary, then it is shown on the system monitor. 3. Look for histories that begin with w1 in Block 2 and Block 3. In this way, we generate 2 new blocks of possible parts of sentences which, depending on the language being modeled, could be formed starting from w1. One of these blocks is a consequence of the search in Block 2, and will be called Block 21, and the other, a consequence of the search in Block 3, called Block 31. In this way, we generate partial hypotheses of sentences. We believe this constitutes a way of speeding up the process, since the search for the next word in the sequence is limited to Blocks 21 and 31. It is possible that there are no histories that begin with w1, that is, it is ossible that Block 21 and Block 31 are empty. In this case, in the model there are no words that can follow w1, and the recognizer will not admit the sentence and will finish the task of recognition. 4. We receive the next word of the sequence, w2. If it is part of the vocabulary, we search for its occurrence in the entries of Block 21 and Block 31; if not, we reject the sequence. 5. We discard from Blocks 21 and 31 those histories that do not have w2 after w1. If we still have entries that contain w2 following w1, then we show w2 on the screen. If not, we reject the sequence, and the recognition task is terminated. . We return to step 3, working now with w2 instead of with w1. In other words, each time that the decoder arrives at this point, one repeat a sequence of steps, working now with the last word recognized in the process. The recognition of a word sequence can end in two ways: a rejection when the decoder determines that the model is not valid or the sequence is out of context, or else, when the symbol $ is received which is the indicator of end of sentence, in which case we have a grammatically correct sentence. There can be a case in which sequences are rejected that are in context and are grammatically correct. This can be resolved retraining the model with new corpus of the same context. We can observe that the recognizer checks the word sequence received for correctness from the point of view of the grammatical rules associated with the context, and at the same time, if it is part of the context being modeled. Although the context models described can be thought to be a combination of bigrams and trigrams, in this work, we cannot talk about n-gram stochastic models, nor of finite state stochastic automatas [2], since the way in which decoding is done does not use probabilities as they do. In fact, this decoder does not measure the probability that the sequences are being modeled or not, but rather simply determines if it can form a sentence which is in the model, and if not, rejects it. 6. Tests. The following tests were performed: 1. An initial test was performed with a corpus consisting of 160 sentences and paragraphs of different lengths. We worked with lengths that varied between 3 and 63 words. The sentences were taken from a Spanish text about linguistics. The corpus had a total of 2563 words. . The vocabulary that would be handled by the recognizer module and by the generator module was determined. The vocabulary started out with 816 different words. 3. A search was performed on the text, to find Block 1, Block 2, Block 3 and Block 4, which form the context model. This process lasted approximately two hours to execute on a PC Pentium 133 Mhz. 4. Sentence blocks were generated. This process was repeated 30 times. Each block generated consisted of 10 sentences. 5. Sentence recognition was performed. To perform the test, word sequences were given, and then we had to determine if such sequences could re recognized using the context model 6. Some small corpus of 10 to 20 sentences was taken anew, and the process was repeated. 7. Results. 1. Additional corpus can be incorporated incrementally without losing the information codified in previous training. 2. The generated sentences are generally shorter than the ones used in training. 3. The number of sentences generated depends on the size of the training corpus. 4. The number of sentences generated which are valid with regards to grammar and context is 70% of the total that were generated in the tests. 5. The number of recognized sentences that are grammatically correct and that correspond to the context is close to 90% when they are selected so as to be similar to the ones in the corpus. 6. About 90% of the generated sentences are not present in the training corpus, with the exception of some short sentences, made up of 2, 3, 4 and up to 5 words. 7. It is not possible to recognize all the sentences and paragraphs, as they appear in the training corpus. . Conclusions. In incremental fashion, it is possible to obtain ever greater robustness in the recognizer module as in the sentence generator module. This modification of the model is obtained at the price of speed during the adjustment, which will depend on the application and the machine. In the same way that a large number of sentences can be generated, also a large number of sentences can b e recognized. Due to the large quantity of sentences that can be generated, it is also possible to recognize a large uantity of sentences and phrases that do not necessarily pertain to the context being modeled, but do pertain to the same language of the corpus. It is not possible to recognize all the sentences and paragraphs, as they appear in the training corpus, since not all the histories present in the corpus are coded in the system memory. This defect could be corrected by saving all the histories that appear in the text, but this would lead to slower searches when performing recognition or generation tasks. We could say we are doing language modeling, since we believe that the modeling of contexts in this fashion can be extrapolated to the language associated with the contexts. This type of linguistic decoder could work in recognition applications where the size of the vocabulary is on the order of thousands.

Tanning Beds free essay sample

â€Å"CANCER† a six lettered word that carries so much meaning to my family. On Tuesday, May 28, 2011 I arrived home from a long day at school. I could feel it in the air that something was different. Both of my parents displayed long, sad faces. As soon as my mother asked â€Å"can I speak to you alone? † I realized that something was definitely wrong. The words that escaped her mouth is something that I would never imagined hearing from my 48 year old mom. She explained that she went to the doctors office to have a spot on her nose examined. After the doctor received the pathology report, he stated that my mother had basal cell carcinoma, and that surgery was required. As a young 16 year old, I was terrified. My mom had skin cancer. Thankfully, after many procedures, and large excisions from the tip of her nose, the cancer was finally removed. We will write a custom essay sample on Tanning Beds or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page With my mother growing up in the California sun in the 70’s, the importance of skin protection and sunscreen was not emphasized. â€Å"The darker the better† was a popular fad throughout this time. My mom used to lather her body with baby oil and lay out in the burning sun. I am sure she was not thinking at the time that the sun’s exposure would cause such a horrible diagnosis in the future. Now, my younger brother and I will be severely punished if we leave the house without applying sunscreen. Tanned skin should not be a fashion statement, because it will only cause negative effects in the future, such as skin cancer. As early as the Renaissance and Elizabethan eras, women with fair skin were considered to be more attractive than women with tanned skin. Fair-skinned women were those who were upper class and spent most of their time indoors. Dark skinned women were associated with being field workers. Women would apply chalk to their faces to make them appear like porcelain. However, with the turn of the Industrial Revolution the trend for whiteness halted. With the working class now moving from the farm to the factories, pale complexions now belonged to the poor, whereas, the more wealthy types embraced some color by leisurely spending life outdoors. Moreover, in 1923, after catching too much Mediterranean sun, Coco Chanel returned to Paris with bronze skin. This ultimately started the chic movement of sunbathing. With Coppertone’s Quick Tan in 1960 and German scientist Friedrich Wolff’s invention of the sun bed in 1978, the popularity of tanning kept rising. However, in 1986, the first SPF 15 was introduced to the public due to medical warnings about too much sun exposure. Unfortunately, people in 1986 failed to take those medical warnings seriously. In today’s modern society, tanning is a basic beauty essential to many women. Many young women tend to resort to a convenient, efficient way of maintaining a glowing tan throughout the year, by the use of indoor tanning beds. However, many young teenage girls are unaware of the deadly effects that come along with indoor tanning. Over time, the effects of too much ultraviolet radiation emitted from tanning beds, can lead to wrinkles, age spots, cataracts, loss of skin elasticity, immune system changes, and skin cancers. In order to prevent young, uneducated teenagers from experiencing such side effects, tanning beds should be banned from minors. As mentioned before, the ever-changing culture has a direct impact on the choices made by the people of society. The media, such as television, magazines, and the Internet can play a major role on the attitudes towards tanning. For example, celebrities such as MTV Jersey Shore’s main star Snooki. She is famous for her acronym of GTL, standing for Gym, Tan, Laundry. These are the three things people must do on a daily basis, according to the orange skinned reality star. Furthermore, in 2006, Sarah Palin gained a lot of noteworthy attention by doctors, dermatologists, and fellow politicians whenever she admitted to having a tanning bed installed in the Alaska Governor’s Mansion. Many people thought that it would send a message to young teenagers that it was acceptable to make use of an indoor tanning bed. Lastly, many movie stars and famous celebrities admit that their secret to a â€Å"healthy glow† is by usage of tanning beds. All in all, there is a major cultural disconnection between the risk and desire for a â€Å"healthy glow. † The media and advertisements of a tanned appearance is ultimately influencing many young teenage girls. Young teenagers are easily persuaded, and many will give in to the heavy peer pressure of the media. Teenage girls will be influenced to have a mindset that indoor tanning will make more people will like and accept them. Or perhaps, with the use of indoor tanning, their self image will mirror the models in the advertising campaigns. However, there are many risks that are involved with the use of tanning beds. According to the American Academy of Dermatology, on an average day, more than one million people tan in tanning beds. Seventy percent of people who use tanning beds are Caucasian girls aged between 16 and 29. DeAnn Lazovich, a professor in epidemiology reports data in her journal, Indoor Tanning and Risk of Melanoma, that â€Å"from 116 cities in the United States found that the average number of tanning salons exceeded the number of McDonalds or Starbucks. † This shows that not only are tanning salons becoming more popular, but with their increasing density, they are becoming more available to people. Overexposure to UV radiation can lead to premature skin aging, discoloration, loss of elasticity, eye damage, and immune suppression. Indoor tanning beds can cause cancer, in non-malignant and malignant forms. Non-malignant types of cancer grow in the first few layers of the skin. Non-malignant forms of skin cancer include basal cell carcinomas or squamous cell carcinomas. Fortunately, these two types of cancer are not as deadly as melanoma, due to the fact that they rarely spread to lymph nodes and vital organs. Moreover, excessive exposure to UVA radiation can cause malignant melanoma, the most dangerous form of skin cancer. Skin melanomas also know as skin cancer is a disease in which skin cells lose the ability to divide and grow normally. Melanoma is life-threatening and deadly. In DeAnn Lazovich’s book Shedding Light On Indoor Tanning, she says that â€Å"melanoma is the most lethal form of skin cancer, but non-melanomas can cause significant morbidity and ever mortality as well. † UV exposure from tanning beds can not only cause skin defects in the future, it can also cause deadly cancers that are sometimes hard to stop. Once the skin cells are damaged, the cancer starts spreading to other organs in the body. In a Radio SmartTalk with Scott Lamar, he featured Doctor Gavin Robertson, who has studied melanoma extensively. When asked about the main cause of skin cancer, Dr. Robertson said â€Å"sunburns are the primary factors of the cause of lethal melanoma, either being from UV rays from the sun or UV rays emitted from tanning beds. † Dr. Robertson further explained the deadly danger of melanoma, by claiming, â€Å"melanoma grows into the layers of your skin hitting blood vessels, and these cells circulate throughout your blood, and start to develop tumors in different organs such as the liver, lungs, and the brain. † Melanoma triggers mutations that make it very easy to multiply and to rapidly form tumors. If melanoma spreads to other organs in the body, there is a very low chance of survival rate. Tanning devices are carcinogenic. Many teenage girls are aware that indoor tanning can cause cancer, however they ignore this because of the worldwide belief that if, by chance, they do get skin cancer, they can easily get it removed. However, even with non-melanoma types of skin cancer, the removal of the skin cancer, can cause major deformities on your body. With these high risks of body deformities, and deadly cancers why would any teenage girl continue to make use of indoor tanning beds? Unfortunately, many teenage girls have higher motivations to tan that outweigh the risks of high levels of UV exposure. DeAnn Lazovih explains that â€Å"perhaps because tanning is commonly perceived as a cosmetic fad, its association with fatal skin cancer is not taken as seriously as it could be,† in Shedding Light on Indoor Tanning. One factor that motivates teenage girls is the importance of appearance enhancement. In today’s culture, attractiveness, fitness, youthfulness, and vitality are all very important factors to many young women. It is claimed by many that tanning will make someone appear â€Å"thinner† and â€Å"younger. † Many teenagers begin using indoor tanning beds to make themselves â€Å"better looking. † However, once again, teenage girl are only looking at the the immediate effects. If they were educated about the effects of indoor tanning, they would know that whenever they hit the age of forty they are going to have wrinkles and dark spots because they failed to protect their skin at a young age. An interview with Amelia Kaymen, a dermatologist in San Francisco, California, was conducted by Ivanhoe Broadcast News about the effects of indoor tanning. When asked how much damage is done before a person reaches age 18, she concluded that â€Å"at least 75 percent of the significant damage is done by the time you’re 18. † This proves that if tanning beds are banned from minors, there is a very high chance the percentage of melanoma and skin cancer would decrease. Another factor as to why indoor tanning is so popular is that many people that live in colder climates often find an efficient and convenient way to maintain a tan throughout the year. In addition to these factors, another main reason people still use indoor tanning beds is for mood enhancement. Whenever people are tan, they often feel better about themselves, because their self image is enhanced by the fact that they have a healthy glow. However, what teenagers do not understand, is that there are many other activities they can indulge in that much safer; such as running, seeing friends, or even applying a tan from a bottle. All of these factors contribute to the popularity of indoor tanning among teenage girls. Furthermore, many teenage girls become dependent on indoor tanning beds. This dependency can turn into an addiction. Tanning bed users only see the immediate effects of a bright, glowing tan. This is why many become addicted to tanning, also known as â€Å"tanorexia. † In Meg Cassidy’s Skin Cancer Kills she introduces Bryan Adlnoff, a professor in drug and alcohol abuse research to further explain the addiction people have with indoor tanning. Adlnoff describes that he â€Å"saw activation in the areas of these participants’ brains that are associated with reward-the kind of brain activity that keeps us coming back for more. † As people step out of the tanning beds, their first reaction is amazement and happiness. Despite the dangers, many teenage girls are only focused on the way the tan makes them look, making it hard to stop frequenting tanning salons. This makes people create a routine out of coming back to the tanning salons and tanning under harmful UV radiation. This is very harmful to their DNA skin cells, eyes, and immune system; however, the users are only looking at the immediate effects instead of taking time to realize what will happen in the long run. Addiction to tanning can be just as harmful to the body just as addiction to cigarettes and drugs are. Now the question is: why do owners of tanning salons continue to encourage people to use indoor tanning, despite the deadly side effects it has on people? First and foremost, tanning salons will always be in business as much as enforcers try to ban them. Tanning and cigarettes are very similar in this case. At the expense of health and safety, people will still stay true to their own mindset. However, tanning companies brainwash their customers by claiming that indoor tanning is a good way to get the daily intake of vitamin D. Vitamin D is also known as the â€Å"sunshine vitamin. † It is very important for bone health and colon cancer prevention, and it is recommended that one should be exposed to an adequate amount of sunlight. However, it is sometimes overrated, especially by tanning salon owners. In Alice Park’s article, Assessing the Risks of Tanning Beds, she introduces Dan Humiston, president of the Indoor Tanning Association. He defends indoor tanning by explaining that, â€Å"most people are vitamin D deficient, and one of the easiest ways to prevent that- it’s simple, it’s free, is to go out in the sun. Or go in a tanning bed, and your skin produces vitamin D. † While this may be true, ask any doctor and they would explain a more educated approach upon receiving vitamin D. In the interview, as mentioned before, with Dr Kaymen, she was once again asked â€Å"how can people balance the need for vitamin D with the risk of sun exposure? †, Dr Kaymen responded that, â€Å"it takes minute amounts of sunlight to manufacture enough vitamin D to stay healthy, and in fact a lot of our foods are supplemented with vitamin D so you probably don’t need to go outside at all. † Doctor Kaymen’s response is a lot more trustworthy because it is coming from a doctor. Despite the claim tanning companies make, someone does not need to depend on sun exposure to acquire the right amount of vitamin D. In addition, another myth from tanning companies is that they claim that indoor tanning is a safer alternative than sunbathing at the beach or out by the pool. Most tanning salon owners and employees are aware and educated of the dangers of indoor tanning, however they continue to encourage people to tan because they ultimately still need business. Sun exposure in general is very harmful to individuals, however the type of UV radiation that is emitted from tanning beds is a lot more direct than the UV radiation from the sun. In Sarah Glynn’s journal Tanning Bed Cancer Risk Double That of Summer Sun, she explains that a team of researchers at the University of Dundee, Scotland confirmed that, â€Å"the risk of skin cancer was six times higher from one of the tanning beds, compared to direct natural sunlight exposure. † This is yet another reason as to why teenage girls should stay away from tanning beds. Applying sunscreen continuously throughout the day as one is in the sun is acceptable. However, applying tanning lotion and laying in a carcinogenic tanning bed for fifteen minutes should be banned, especially from young teenagers who are not fully educated on the effects and dangers of indoor tanning. To repeat, with the over-empowering dangers of UV exposure from tanning beds, it is only in the best judgement as a society to put restrictions on the usage of these carcinogenic machines. Referring back to the interview with Amelia Kaymen, she said that most of the damage was done before a person reaches the age of 18. When asked if tanning booths should be illegal for minors, she responded, â€Å"I would agree with prohibiting a person under the age of 18 going without parental permission. † Fortunately, according to the American Academy of Dermatology, â€Å"on October 9, 2011, California became the first state in the nation to prohibit the use of indoor tanning devices for all children and adolescents under the age of 18. † Along with California, Vermont, Illinois, Texas, Connecticut, Nevada, and New Jersey also put an age restriction on the indoor tanning. Furthermore, â€Å"on May 6, 2013, U. S. Food and Drug Administration issued a proposed order for stricter regulations on indoor tanning devices. † Everyone who frequents tanning salons should be educated about the dangerous effects of indoor tanning. Everyone should be able to locate, read, and understand the warning labels on every tanning machine. Also, states who have not yet put an age restriction on indoor tanning should at least put restrictions on parent consent. Parents must have the right to know that their children are involving themselves in this dangerous type of behavior and activity, without fully understanding the consequences. Lastly, the indoor tanning companies need to step up and communicate the importance of UV protection, both in the tanning beds and outside in the harmful sun.

Saina Neval free essay sample

Saina Nehwal (born March 17, 1990) is an Indian Khel Ratna winning badminton player currently ranked number 2 in the world by Badminton,[4]. Saina is the first Indian woman to reach the singles quarterfinals at the Olympics and the first Indian to win the World Junior Badminton Championships. Saina Nehwal made history on June 21, 2009, becoming the first Indian to win a Super Series tournament, by clinching the Indonesia Open with a stunning victory over higher-ranked Chinese Wang Lin in Jakarta. The Super Series tournament is roughly equivalent to a Grand Slam in tennis). Saina won her second career Super Series title by winning the Singapore Open title on June 20, 2010. She completed a hat-trick in the same year by winning the Indonesian Open on June 27, 2010. This win resulted in her rise to 3rd ranking and subsequently to No. 2. Later in the same year she also won Hong Kong Super Series on December 12, 2010. Previously coached by S. We will write a custom essay sample on Saina Neval or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page M. Arif, a Dronacharya Award winner, Saina is the reigning Indian national junior champion and is currently coached by Indonesian badminton legend Atik Jauhari since August 2008,[5] with the former All England champion and national coachPullela Gopichand being her mentor. Saina was born in Hisar, Haryana and has spent her entire life in Hyderabad, India. Her foray into the world of badminton was influenced by her father Dr. Harvir Singh, a scientist at the Directorate of Oilseeds Research, Hyderabad and her mother Usha Nehwal[1], both of whom were former badminton champions in Haryana. She is the top ranked player (women) in Indian Badminton history. [6] Childhood and early training Saina was born in 1990 in a Jat family from Haryana. Her birth was a big disappointment to her grandmother as grandma wanted a boy [7] In December 1998, Sainas father took her to meet Coach Nani Prasad at the Lal Bahadur Shastri Stadium in Hyderabad. Seeing potential in the girl, Prasad asked Singh to enroll Saina as a summer trainee. Harvir Singh and Saina, who was 8 years old at the time, would wake at 4am every morning and head to the stadium which was 25 km away. After two hours of practice, Singh would drop Saina at school on his way to work. Sitting behind her father on his scooter, Saina would often fall asleep on these journeys which prompted her mother to accompany them for the next three months. In order to keep up with the rising cost of her training, Saina’s father withdrew money from his savings and provident fund. The tight-rope walk continued until 2002, when sports brand Yonex offered to sponsor Saina’s kit. As her status and rankings improved, the sponsorships increased. In 2004, BPCL (Bharat Petroleum Corporation Limited). [8] signed the rising star onto their payroll, and she is also supported by Olympic Gold Quest. [9] Saina Nehwal won 2010 Commonwealth games gold in Womens Singles Shuttle badminton held in Siri Fort Auditorium, Delhi,on 14th October 2010.

San Francisco State - SAT Scores and Admissions Data

San Francisco State - SAT Scores and Admissions Data San Francisco State University Admissions Overview: As part of the application, students will need to submit scores from the SAT or ACT. While the majority of applicants submit SAT scores, the university accepts both equally. For more information, check out the schools website or contact the admissions office. Will You Get In? Calculate Your Chances of Getting In  with this free tool from Cappex Admissions Data (2016): San Francisco State University Acceptance Rate: 68%SF State GPA, SAT and ACT Score GraphTest Scores 25th / 75th PercentileSAT Critical Reading: 430 / 540SAT Math: 430 / 550SAT Writing: - / -What these SAT numbers meanCompare Cal State SAT ScoresACT Composite: 18 / 24ACT English: 16  / 24ACT Math: 17 / 24What these ACT numbers meanCompare Cal State ACT Scores San Francisco State Description: Founded in 1899, San Francisco State University takes pride in the diversity of its student body. 67% of undergraduates are students of color. Students come from 94 countries, and the school enrolls more international students than any other masters degree-granting university in the U.S. San Francisco State offers 115 bachelors and 95 masters programs. The 142-acre urban campus gives students ready access to the dining and cultural attractions of the city. In athletics, the San Francisco State Gators compete in the NCAA Division II  California Collegiate Athletic Association. Popular sports include softball, cross country, basketball, soccer, and wrestling.  SFS is one of the  23 Cal State schools. Enrollment (2016): Total Enrollment: 29,045  (25,945 undergraduates)Gender Breakdown: 44% Male / 56% Female83% Full-time Costs (2016- 17): Tuition and Fees: $6,484  (in-state); $17,644  (out-of-state)Books: $1,900  (why so much?)Room and Board: $13,882Other Expenses: $2,966Total Cost: $25,232  (in-state); $36,392 (out-of-state) San Francisco State Financial Aid (2015- 16): Percentage of New Students Receiving Aid: 76%Percentage of New Students Receiving Types of AidGrants: 69%Loans: 42%Average Amount of AidGrants: $8,817Loans: $5,441 Academic Programs: Most Popular Majors:  Biology, Business Administration, Criminal Justice, Early Childhood Education, English, Film Studies, Liberal Arts and Sciences, Psychology, Radio and Television, Speech and Rhetorical Studies What major is right for you?  Sign up to take the free My Careers and Majors Quiz at Cappex. Transfer, Graduation and Retention Rates: First Year Student Retention (full-time students): 80%Transfer Out Rate: 6%4-Year Graduation Rate: 18%6-Year Graduation Rate: 53% Intercollegiate Athletic Programs: Mens Sports:  Soccer, Wrestling, Cross Country, Baseball, BasketballWomens Sports:  Softball, Volleyball, Track and Field, Basketball, Soccer, Cross Country Data Source: National Center for Educational Statistics Admissions Profiles for Other Cal State Campuses Bakersfield | Channel Islands | Chico | Dominquez Hills | East Bay | Fresno State | Fullerton | Humboldt | Long Beach | Los Angeles | Maritime | Monterey Bay | Northridge | Pomona (Cal Poly) | Sacramento | San Bernardino | San Diego | San Francisco | San Jose State | San Luis Obispo (Cal Poly) | San Marcos | Sonoma State | Stanislaus More California Public University Information SAT Score Comparison for Cal State SchoolsACT Score Comparison for Cal State SchoolsThe University of California SystemSAT Score Comparison for the UC SystemACT Score Comparison for the UC System

Impact of Racism in the School Environment Research Paper

Impact of Racism in the School Environment - Research Paper Example Academically, a student that suffers from bullying will have low self-esteem and lack the push he or she requires to perform well in school. They may be shy in presenting themselves in the school and have no morale to continue with learning. It is common to see students that suffer from bullying want transfers to other schools. It means that the victim hates the school and would not concentrate on any activity. Their grades in the class drop as they slow or even stop studying in the fear of attack. As a part of the parents protecting their children, they opt to take them to other schools. If the school does not find a solution to bullying, the parent must protect the child (Burgis, 2012). As a measure to curb the harsh results of racism, schools have launch zero tolerance to racism policies. It ensures that students get the appropriate education as expected and do not suffer from any negative forces. The victims of racism are in three groups; students, teachers, and the schools itsel f. While we appreciate cultural diversity, racists take these differences and use them to intimidate others. It is not only students that suffer from racism. A teacher whose culture does not conform to that of the environment of operation may face racism and suffer its effects. The schools' atmosphere is equally affected when bullying and racism are in progress.   Students that face racism may be afraid to go to school. When they remember the embarrassing moments, they resist any attempts to have them go back to the same place.  Ã‚