Jungian archetypes Essay Example for Free

Jungian archetypes Essay After reading the texts assigned for this week, I have selected â€Å"The Odjibwa Corn Hero† for my first response paper for two reasons: a) it is the only story I read that made me want to eat a bucket of corn afterwards and b) even though I do not know anything about Native American folklore, the story seems to have something familiar at its core which I intend to uncover. The story begins with Wunzh, after reaching a proper age, he decides to go to an isolated place to â€Å"fast undisturbed and find his guardian in life†. He was often baffled by the wonders of the world and searched for something that would allow himn  to help his people so that they wouldn’t have to â€Å"rely on the luck of the hunt or the occasional fish†. Exhausted, yet still praying for an answer on the third day of his fasting a figure appeared â€Å"dressed in yellow and green garments† claiming that the Great Spirit had sent him to grant him his wish provided that he fought him. Three trials the hero undergoes and eventually beats the figure, which then it gives him instructions on how to strip it of its clothes, plant and care for it; he did as instructed until one day he returned only to find â€Å"a tall, graceful plant, with clusters of yellow on  its side, long green leaves†; its name was â€Å"Mondawmin†. The hero then, showed his family how to plant and how to cook this plant and everyone lived happily ever after. My first thought after reading the myth, is that it may be simple in form but deep in the messages it tries to convey: fasting, meditation, and isolation are tools which the hero uses to cleanse and prepare himself as he tries to reach spiritual transcendence; a kind of rite of passage from boyhood to manhood, for the weaker the body the stronger the mind as they say; and the one who proves worthy and courageous shall taste the â€Å"fruits† of his labor.

Management Philosophy Essay Example for Free

Management Philosophy Essay Diversity trainer through the National Multi-Cultural Institute (NMCI) which is based out of Washington DC. Bahaudin worked as a manager, an Internal Consultant, Trainer, and Teacher at the Education and Training Development Department of Human Resources with Publix Super Markets Inc. for sixteen years. Bahaudin has been a visitor or speaker on conferences in the United States of America, Vietnam, Malaysia, Afghanistan, Pakistan, India, Brazil, Jamaica, Bahamas, St. Lucia, Thailand, Myanmar (Burma), Grenada, and several other Caribbean countries. Bahaudin was born in Khoshie of Logar and raised in Kabul of Afghanistan. Bahaudin finished his high school degree and higher education in the United States. Management Philosophy: Some of Bahaudin’s favorite management concepts, which he has used in practice, happen to be Self-fulfilling Prophecy, Theory Y View of Motivation, Management by Objective and Management by Walking Around. Managers are likely to get exactly what they expect from themselves and their employees. Bahaudin believes that most people want to do a good job, especially when they are given the right tools, educational developments and performance opportunities. He prefers leading people and managing systems. Bahaudin likes to clarify his overall objectives, set realistic goals and then work to achieve them in a realistic timeframe. According to Bahaudin, the journey of working toward the achievement of one’s goals itself can certainly be one way to happiness. As they say, happiness is the way. Bahaudin truly believes that happiness is a journey, and not a destination. Happiness is the progressive realization of worthwhile and predetermined goals. So, set your goals and, as someone said, then â€Å"work like you dont need money; study like you are a noble prize winner; love like youve never been hurt; and dance like no ones watching. Have a positive attitude and, when possible, make a difference in at least one person’s life. Remember, if you can perceive and believe a better state of being, then you are very likely to achieve it as well. Overall, learn as much as you can; stretch yourself as far as possible, but not beyond; never settle for less than your capabilities; aim for total integrity; and be the best that you can be! As an effective manager and leader, may you have the hindsight to know where you have been; the foresight to know where you are going; and the insight to know when you are about to go too far.

Compression Techniques used for Medical Image

Compression Techniques used for Medical Image 1.1 Introduction Image compression is an important research issue over the last years. A several techniques and methods have been presented to achieve common goals to alter the representation of information of the image sufficiently well with less data size and high compression rates. These techniques can be classified into two categories, lossless and lossy compression techniques. Lossless techniques are applied when data are critical and loss of information is not acceptable such as Huffman encoding, Run Length Encoding (RLE), Lempel-Ziv-Welch coding (LZW) and Area coding. Hence, many medical images should be compressed by lossless techniques. On the other hand, Lossy compression techniques such as Predictive Coding (PC), Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Vector Quantization (VQ) more efficient in terms of storage and transmission needs but there is no warranty that they can preserve the characteristics needed in medical image processing and diagnosis [1-2]. Data compression is the process that transform data files into smaller ones this process is effective for storage and transmission. It presents the information in a digital form as binary sequences which hold spatial and statistical redundancy. The relatively high cost of storage and transmission makes data compression worthy. Compression is considered necessary and essential key for creating image files with manageable and transmittable sizes [3]. The basic goal of image compression is to reduce the bit rate of an image to decrease the capacity of the channel or digital storage memory requirements; while maintaining the important information in the image [4]. The bit rate is measured in bits per pixel (bpp). Almost all methods of image compression are based on two fundamental principles: The first principle is to remove the redundancy or the duplication from the image. This approach is called redundancy reduction. The second principle is to remove parts or details of the image that will not be noticed by the user. This approach is called irrelevancy reduction. Image compression methods are based on either redundancy reduction or irrelevancy reduction separately while most compression methods exploit both. While in other methods they cannot be easily separated [2]. Several image compression techniques encode transformed image data instead of the original images [5]-[6]. In this thesis, an approach is developed to enhance the performance of Huffman compression coding a new hybrid lossless image compression technique that combines between lossless and lossy compression which named LPC-DWT-Huffman (LPCDH) technique is proposed to maximize compression so that threefold compression can be obtained. The image firstly passed through the LPC transformation. The waveform transformation is then applied to the LPC output. Finally, the wavelet coefficients are encoded by the Huffman coding. Compared with both Huffman, LPC- Huffman and DWT-Huffman (DH) techniques; our new model is as maximum compression ratio as that before. However, this is still needed for more work especially with the advancement of medical imaging systems offering high resolution and video recording. Medical images come in the front of diagnostic, treatment and fellow up of different diseases. Therefore, nowadays, many hospitals around the world are routinely using medical image processing a nd compression tools. 1.1.1 Motivations Most hospitals store medical image data in digital form using picture archiving and communication systems due to extensive digitization of data and increasing telemedicine use. However, the need for data storage capacity and transmission bandwidth continues to exceed the capability of available technologies. Medical image processing and compression have become an important tool for diagnosis and treatment of many diseases so we need a hybrid technique to compress medical image without any loss in image information which important for medical diagnosis. 1.1.2 Contributions Image compression plays a critical role in telemedicine. It is desired that either single images or sequences of images be transmitted over computer networks at large distances that they could be used in a multitude of purposes. The main contribution of the research is aim to compress medical image to be small size, reliable, improved and fast to facilitate medical diagnosis performed by many medical centers. 1.2 Thesis Organization The thesis is organized into six chapters, as following: Chapter 2 Describes the basic background on the image compression technique including lossless and lossy methods and describes the types of medical images Chapter 3 Provides a literature survey for medical image compression. Chapter 4 Describes LPC-DWT-Huffman (proposed methods) algorithm implementation. The objective is to achieve a reasonable compression ratio as well as better quality of reproduction of image with a low power consumption. Chapter 5 Provides simulation results of compression of several medical images and compare it with other methods using several metrics. Chapter 6 Provides some drawn conclusions about this work and some suggestions for the future work. Appendix A Provides Huffman example and comparison between the methods for the last years. Appendix B Provides the Matlab Codes Appendix C Provides various medical image compression using LPCDH. 1.3 Introduction Image compression is the process of obtaining a compact representation of an image while maintaining all the necessary information important for medical diagnosis. The target of the Image compression is to reduce the image size in bytes without effects on the quality of the image. The decrease in image size permits images to save memory space. The image compression methods are generally categorized into two central types: Lossless and Lossy methods. The major objective of each type is to rebuild the original image from the compressed one without affecting any of its numerical or physical values [7]. Lossless compression also called noiseless coding that the original image can perfectly recover each individual pixel value from the compressed (encoded) image but have low compression rate. Lossless compression methods are often based on redundancy reduction which uses statistical decomposition techniques to eliminate or remove the redundancy (duplication) in the original image. Lossless Image coding is also important in applications where no information loss is allowed during compression. Due to the cost, it is used only for a few applications with stringent requirements such as medical imaging [8-9]. In lossy compression techniques there are a slight loss of data but high compression ratio. The original and reconstructed images are not perfectly matched. However, practically near to each other, this difference is represented as a noise. Data loss may be unacceptable in many applications so that it must be lossless. In medical images compression that use lossless techniques do not give enough advantages in transmission and storage and the compression that use lossy techniques may lose critical data required for diagnosis [10]. This thesis presents a combination of lossy and lossless compression to get high compressed image without data loss. 1.4 Lossless Compression If the data have been lossless compressed, the original data can be exactly reconstructed from the compressed data. This is generally used for many applications that cannot allow any variations between the original and reconstructed data. The types of lossless compression can be analyzed in Figure 2.1. Figure 2.1: lossless compression Run Length Encoding Run length encoding, also called recurrence coding, is one of the simplest lossless data compression algorithms. It is based on the idea of encoding a consecutive occurrence of the same symbol. It is effective for data sets that are consist of long sequences of a single repeated character [50]. This is performed by replacing a series of repeated symbols with a count and the symbol. That is, RLE finds the number of repeated symbols in the input image and replaces them with two-byte code. The first byte for the number and the second one is for the symbol. For a simple illustrative example, the string AAAAAABBBBCCCCC is encoded as A6B4C5; that saves nine bytes (i.e. compression ratio =15/6=5/2). However in some cases there is no much consecutive repeation which reduces the compression ratio. An illustrative example, the original data 12000131415000000900, the RLE encodes it to 120313141506902 (i.e. compression ratio =20/15=4/3). Moreover if the data is random the RLE may fail to achieve any compression ratio [30]-[49]. Huffman encoding It is the most popular lossless compression technique for removing coding redundancy. The Huffman encoding starts with computing the probability of each symbol in the image. These symbols probabilities are sorted in a descending order creating leaf nodes of a tree. The Huffman code is designed by merging the lowest probable symbols producing a new probable, this process is continued until only two probabilities of two last symbols are left. The code tree is obtained and Huffman codes are formed from labelling the tree branch with 0 and 1 [9]. The Huffman codes for each symbol is obtained by reading the branch digits sequentially from the root node to the leaf. Huffman code procedure is based on the following three observations: 1) More frequently(higher probability) occurred symbols will have shorter code words than symbol that occur less frequently. 2) The two symbols that occur least frequently will have the same length code. 3) The Huffman codes are variable length code and prefix code. For more indication Huffman example is presented in details in Appendix (A-I). The entropy (H) describes the possible compression for the image in bit per pixel. It must be noted that, there arent any possible compression ratio smaller than entropy. The entropy of any image is calculated as the average information probability [12]. (2.1) Where Pk is the probability of symbols, k is the intensity value, and L is the number of intensity values used to present image. The average code length is given by the sum of product of probability of the symbol and number of bits used to encode it. More information can be founded in [13-14] and the Huffman code efficiency is calculated as (2.2) LZW coding LZW (Lempel- Ziv Welch) is given by J. Ziv and A. Lempel in 1977 [51].T. Welchs refinements to the algorithm were published in 1984 [52]. LZW compression replaces strings of characters with single codes. It does not do any analysis of the input text. But, it adds every new string of characters to a table of strings. Compression occurs when the output is a single code instead of a string of characters. LZW is a dictionary based coding which can be static or dynamic. In static coding, dictionary is fixed during the encoding and decoding processes. In dynamic coding, the dictionary is updated. LZW is widely used in computer industry and it is implemented as compress command on UNIX [30]. The output code of that the LZW algorithm can be any arbitrary length, but it must have more bits than a single character. The first 256 codes are by default assigned to the standard character set. The remaining codes are assigned to strings as the algorithm proceeds. There are three best-known applications of LZW: UNIX compress (file compression), GIF image compression, and V.42 bits (compression over Modems) [50]. Area coding Area coding is an enhanced form of RLE. This is more advance than the other lossless methods. The algorithms of area coding find rectangular regions with the same properties. These regions are coded into a specific form as an element with two points and a certain structure. This coding can be highly effective but it has the problem of a nonlinear method, which cannot be designed in hardware [9]. 1.5 Lossy Compression Lossy Compression techniques deliver greater compression percentages than lossless ones. But there are some loss of information, and the data cannot be reconstructed exactly. In some applications, exact reconstruction is not necessary. The lossy compression methods are given in Figure 2.2. In the following subsections, several Lossy compression techniques are reviewed: Figure 2.2: lossy compression Discrete Wavelet Transform (DWT) Wavelet analysis have been known as an efficient approach to representing data (signal or image). The Discrete Wavelet Transform (DWT) depends on filtering the image with high-pass filter and low-pass filter.in the first stage The image is filtered row by row (horizontal direction) with two filters and and down sampling (keep the even indexed column) every samples at the filter outputs. This produces two DWT coefficients each of size N ÃÆ'-N/2. In the second stage, the DWT coefficients of the filter are filtered column by column (vertical direction) with the same two filters and keep the even indexed row and subsampled to give two other sets of DWT coefficients of each size N/2ÃÆ'-N/2. The output is defined by approximation and detailed coefficients as shown in Figure 2.3. Figure 2.3: filter stage in 2D DWT [15]. LL coefficients: low-pass in the horizontal direction and lowpass in the vertical direction. HL coefficients: high-pass in the horizontal direction and lowpass in the vertical direction, thus follow horizontal edges more than vertical edges. LH coefficients: high-pass in the vertical direction and low-pass in the horizontal direction, thus follow vertical edges than horizontal edges. HH coefficients: high-pass in the horizontal direction and high-pass in the vertical direction, thus preserve diagonal edges. Figure 2.4 show the LL, HL, LH, and HH when one level wavelet is applied to brain image. It is noticed that The LL contains, furthermore all information about the image while the size is quarter of original image size if we disregard the HL, LH, and HH three detailed coefficients shows horizontal, vertical and diagonal details. The Compression ratio increases when the number of wavelet coefficients that are equal zeroes increase. This implies that one level wavelet can provide compression ratio of four [16]. Figure 2.4: Wavelet Decomposition applied on a brain image. The Discrete Wavelet Transform (DWT) of a sequence consists of two series expansions, one is to the approximation and the other to the details of the sequence. The formal definition of DWT of an N-point sequence x [n], 0 à ¢Ã¢â‚¬ °Ã‚ ¤ n à ¢Ã¢â‚¬ °Ã‚ ¤ N à ¢Ã‹â€ Ã¢â‚¬â„¢ 1 is given by [17]: (2.3) (2.4) (2.5) Where Q (n1 ,n2) is approximated signal, E(n1 ,n2) is an image, WQ (j,k1,k2) is the approximation DWT and W µ (j,k1,k2) is the detailed DWT where i represent the direction index (vertical V, horizontal H, diagonal D) [18]. To reconstruct back the original image from the LL (cA), HL (cD(h)), LH (cD(v)), and HH (cD(d)) coefficients, the inverse 2D DWT (IDWT) is applied as shown in Figure 2.5. Figure 2.5: one level inverse 2D-DWT [19]. The equation of IDWT that reconstruct the image E () is given by [18]: (2.6) DWT has different families such as Haar and Daupachies (db) the compression ratio can vary from wavelet type to another depending which one can represented the signal in fewer number coefficients. Predictive Coding (PC) The main component of the predictive coding method is the Predictor which exists in both encoder and decoder. The encoder computes the predicted value for a pixel, denote xˆ (n), based on the known pixel values of its neighboring pixels. The residual error, which is the difference value between the actual value of the current pixel x (n) and x ˆ (n) the predicted one. This is computed for all pixels. The residual errors are then encoded by any encoding scheme to generate a compressed data stream [21]. The residual errors must be small to achieve high compression ratio. e (n) = x (n) xˆ (n)(2.7) e (n) =x(n) (2.8) Where k is the pixel order and ÃŽÂ ± is a value between 0 and 1 [20]. The decoder also computes the predicted value of the current pixel xˆ  (n) based on the previously decoded color values of neighboring pixels using the same method as the encoder. The decoder decodes the residual error for the current pixel and performs the inverse operation to restore the value of the current pixel [21]. x (n) = e (n) + xˆ  (n)(2.9) Linear predictive coding (LPC) The techniques of linear prediction have been applied with great success in many problems of speech processing. The success in processing speech signals suggests that similar techniques might be useful in modelling and coding of 2-D image signals. Due to the extensive computation required for its implementation in two dimensions, only the simplest forms of linear prediction have received much attention in image coding [22]. The schemes of one dimensional predictors make predictions based only on the value of the previous pixel on the current line as shown in equation. Z = X D(2.10) Where Z denotes as output of predictor and X is the current pixel and D is the adjacent pixel. The two dimensional prediction scheme based on the values of previous pixels in a left-to-right, top-to-bottom scan of an image. In Figure 2.6 X denotes the current pixel and A, B, C and D are the adjacent pixels. If the current pixel is the top leftmost one, then there is no prediction since there are no adjacent pixels and no prior information for prediction [21]. Figure 2.6: Neighbor pixels for predicting Z = x (B + D)(2.11) Then, the residual error (E), which is the difference between the actual value of the current pixel (X) and the predicted one (Z) is given by the following equation. E = X Z(2.12) Discrete Cosine Transform (DCT) The Discrete Cosine Transform (DCT) was first proposed by N. Ahmed [57]. It has been more and more important in recent years [55]. The DCT is similar to the discrete Fourier transform that transforms a signal or image from the spatial domain to the frequency domain as shown in Figure 2.7. Figure 2.7: Image transformation from the spatial domain to the frequency domain [55]. DCT represents a finite series of data points as a sum of harmonics cosine functions. DCTs representation have been used for numerous data processing applications, such as lossy coding of audio signal and images. It has been found that small number of DCT coefficients are capable of representing large sequence of raw data. This transform has been widely used in signal processing of image data, especially in coding for compression for its near-optimal performance. The discrete cosine transform helps to separate the image into spectral sub-bands of differing importance with respect to the images visual quality [55]. The use of cosine is much more efficient than sine functions in image compression since this cosine function is capable of representing edges and boundary. As described below, fewer coefficients are needed to approximate and represent a typical signal. The Two-dimensional DCT is useful in the analysis of two-dimensional (2D) signals such as images. We say that the 2D DCT is separable in the two dimensions. It is computed in a simple way: The 1D DCT is applied to each row of an image, s, and then to each column of the result. Thus, the transform of the image s(x, y) is given by [55], (2.13) where. (n x m) is the size of the block that the DCT is applied on. Equation (2.3) calculates one entry (u, v) of the transformed image from the pixel values of the original image matrix [55]. Where u and v are the sample in the frequency domain. DCT is widely used especially for image compression for encoding and decoding, at encoding process image divided into N x N blocks after that DCT performed to each block. In practice JPEG compression uses DCT with a block of 88. Quantization applied to DCT coefficient to compress the blocks so selecting any quantization method effect on compression value. Compressed blocks are saved in a storage memory with significantly space reduction. In decoding process, compressed blocks are loaded which de-quantized with reverse the quantization process. Inverse DCT was applied on each block and merging blocks into an image which is similar to original one [56]. Vector Quantization Vector Quantization (VQ) is a lossy compression method. It uses a codebook containing pixel patterns with corresponding indexes on each of them. The main idea of VQ is to represent arrays of pixels by an index in the codebook. In this way, compression is achieved because the size of the index is usually a small fraction of that of the block of pixels. The image is subdivided into blocks, typically of a fixed size of nÃÆ'-n pixels. For each block, the nearest codebook entry under the distance metric is found and the ordinal number of the entry is transmitted. On reconstruction, the same codebook is used and a simple look-up operation is performed to produce the reconstructed image [53]. The main advantages of VQ are the simplicity of its idea and the possible efficient implementation of the decoder. Moreover, VQ is theoretically an efficient method for image compression, and superior performance will be gained for large vectors. However, in order to use large vectors, VQ becomes complex and requires many computational resources (e.g. memory, computations per pixel) in order to efficiently construct and search a codebook. More research on reducing this complexity has to be done in order to make VQ a practical image compression method with superior quality [50]. Learning Vector Quantization is a supervised learning algorithm which can be used to modify the codebook if a set of labeled training data is available [13]. For an input vector x, let the nearest code-word index be i and let j be the class label for the input vector. The learning-rate parameter is initialized to 0.1 and then decreases monotonically with each iteration. After a suitable number of iterations, the codebook typically converges and the training is terminated. The main drawback of the conventional VQ coding is the computational load needed during the encoding stage as an exhaustive search is required through the entire codebook for each input vector. An alternative approach is to cascade a number of encoders in a hierarchical manner that trades off accuracy and speed of encoding [14], [54]. 1.6 Medical Image Types Medical imaging techniques allow doctors and researchers to view activities or problems within the human body, without invasive neurosurgery. There are a number of accepted and safe imaging techniques such as X-rays, Magnetic resonance imaging (MRI), Computed tomography (CT), Positron Emission Tomography (PET) and Electroencephalography (EEG) [23-24]. 1.7 Conclusion In this chapter many compression techniques used for medical image have discussed. There are several types of medical images such as X-rays, Magnetic resonance imaging (MRI), Computed tomography (CT), Positron Emission Tomography (PET) and Electroencephalography (EEG). Image compression has two categories lossy and lossless compression. Lossless compression such as Run Length Encoding, Huffman Encoding Lempel-Ziv-Welch, and Area Coding. Lossy compression such as Predictive Coding (PC), Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Vector Quantization (VQ).Several compression techniques already present a better techniques which are faster, more accurate, more memory efficient and simpler to use. These methods will be discussed in the next chapter.

Northern Lights and Swallows and Amazons Essay -- Literary Analysis, B

Rudd’s (2009) essay evaluates Enid Blyton’s work offering a different perspective to account for the appeal and popularity of the author. This essay looks at the aspects raised by Rudd. How Blyton, Pullman and Ransom illustrate the different aspects of a good or a bad book. The way critics confer prestige on a book or author and the arising criticism. How the agenda of the committees affects the selection of prize-winners. Finally, looking at the factors involved in success. The set books used in his essay are Pullman (1995) Northern Lights and Ransom (2001) Swallows and Amazons. Critics view the books by Pullman and Ransom as examples of literary excellence. In order to evaluate this opinion it is necessary to discuss what aspects critics consider contribute to a good book and how these books illustrate them. The American Library Associate (ALA) uses the term ‘edubrow’ (Kidd, (2009) p158) to mean the middle ground of literature with an educational emphasis. This emphasis is at the centre of the criteria for a good book by increasing the experiences of the reader through varied language, dynamic themes, rounded characterisation with comprehensive plots. The critics favour works that involve the reader in a non-passive manner to gain insights into universal aspects of human existence like love, identity, revenge, sexuality and betrayal. Pullman has written a basic adventure story laced with multiple themes, metaphors and ideas. He uses intertextuality to enrich his text and enhance his ideas and arguments (Squires, 2009). His novel is mainly a critique of the theology surrounding the Judaeo-Christian myth of the Fall where the gaining of experience replaces the loss of innocence. He compares this idea with the journey of his m... ...as created controversy where his books are studied and dissected by academics. He is outspoken and interacts with critics about the themes in his book, which are the antithesis of C.S Lewis Narnia series. Controversy and debate are forms of creating interest in a book that send sales soaring. Everyone wants to read the book that is creating such a furore. In conclusion, critical evaluation of what makes a book good or bad depends on the selection criteria and agenda of those making the evaluation. The prizes have been criticised through the years and the selection committees have risen to this by changing the selection process, even if this change has been slow. Children’s Literature is in flux due to the ever-changing ideas and perceptions of childhood. Children’s books seen as prestigious today may become, like Blyton, unpalatable to the critics of tomorrow.

Drinking on College Campuses :: beer Alcohol Abuse Alcoholism,

Drinking on College Campuses Beer bongs, keg stands, and a million new drinks to discover, these are what college is all about. First-year students are introduced to a whole new world of parties that last until 3 a.m. and drinking beer for the usual breakfast. The week consists of concentrating on school for about 4 days of the week and partying 3 days. The money that was supposed to go towards books and gas to get home has been hoarded for the latest beer run or was used to get into the bar. This trend is getting into the habit of drinking as you enter college; it seems the two go hand in hand. It has become a rite of passage that weaved its way into the introduction of university life (National Institute, October 2002). Those students who never drank in high school seem to think drinking is suddenly okay when they start studying for their bachelor’s degree. This addition of responsibility is then balanced by the act of partying. It seems completely absurd that students choose to drink while investing around $20,000 a year in school. It all starts at high school graduation. Drinking is suddenly endorsed, or protested less, by parents, coaches, adults, organizations, and businesses. When seniors in high school finally graduate, it is common for a party to be thrown in their honor. Some of these parties include alcohol, and we can be pretty sure it wasn’t bought by the graduate unless they flunked a few times and are of legal age. Parents, other adults, and older friends supply the liquor and beer for the underage partiers. When the graduates make the next major step in their life and head for college, they are confronted with many opportunities to get hammered, sloshed, annihilated, drunk, inebriated, intoxicated, wasted, and totally smashed. Other college students are eager to help their young, new friends out by taking them for a trip to the liquor store. Since some bars are legal to those over the age of eighteen, it’s not a problem getting served there either. The 21 year-olds are conveniently stamped for minors looking to spot a potential buyer. Since a minor isn’t worried about getting served, the most apparent problem is getting to the bar. One setting of this national trend can be studied locally.

Developmental Psychology Journal Articles Essay -- Papers

Developmental Psychology Journal Articles The five journal articles I examined were all from a journal titled Developmental Psychology, May 2000. The first journal article that I observed was "Sleep Patterns and Sleep Disruptions in School-Aged Children." This study assessed the sleep patterns, sleep disruptions, and sleepiness of school-age children. Sleep patterns of 140 children (72 boys and 68 girls; 2nd-, 4th-, and 6th-grade students) were evaluated with activity monitors (actigraphs). In addition, the children and their parents completed complementary sleep questionnaires and daily reports. The findings reflected significant age differences, indicating that older children have more delayed sleep onset times and increased reported daytime sleepiness. Girls were found to spend more time in sleep and to have an increased percentage of motionless sleep. Fragmented sleep was found in 18% of the children. No age differences were found in any of the sleep quality measures. Scores on objective sleep measures were associated with subjective reports of sleepiness. Family stress, parental age, and parental education were related to the child's sleep-wake measures. The next article I observed was "Shared Caregiving: Comparisons Between Home and Child-Care Settings." The experiences of 84 German toddlers (12-24 months old) who were either enrolled or not enrolled in child care were described with observational checklists from the time they woke up until they went to bed. The total amount of care experienced over the course of a weekday by 35 pairs of toddlers (1 member of each pair in child care, 1 member not) did not differ according to whether the toddlers spent time in child care. Although the child... ...h their mothers and their fathers on separate occasions in their families' homes. Parent-child pairs played for 8 minutes each with a feminine-stereotyped toy set (foods and plates) and a masculine-stereotyped toy set (track and cars). Levels of affiliation (engaging vs. distancing) and assertion (direct vs. non-direct) were rated on 7-point scales every 5 seconds from the videotapes for both parent and child. Overall, the play activity accounted for a large proportion of the variance in parents' and children's mean affiliation and assertion ratings. Some hypothesized gender-related differences in behavior were also observed. In addition, exploratory analyses revealed some differences between the different ethnic groups. The results highlight the importance of role modeling and activity settings in the socialization and social construction of gender.

Does Robert Louis Stevenson explore the duality of human nature in Dr. Jekyll and Mr Hyde Essay

Written between 1884 and 1887, Robert Louis Stevenson’s novel, â€Å"the strange case of Dr. Jekyll and Mr Hyde†, is about a well respected physician and his ‘other self’ Mr Hyde. Dr. Jekyll is described as a typical Victorian gentleman . [PD1] Dr Jekyll wanted to develop a potion because he believed he could create a perfectly righteous human being by destroying the evil of the mind and body. When he creates this potion, it doesn’t quite go according to plan. He takes the potion for the first time, but when he goes back to normal, he turns into Mr Hyde without taking the potion. Slowly, Mr Hyde starts to take over Dr Jekyll. When Dr Jekyll turns into Mr Hyde, it changes his appearance, because of this, no one wants to approach him or talk to him. During the time when the book was written, people who looked different or who had disabilities or deformities which are widely accepted today, were not liked and were usually shut away. This is why no one liked or talked to Hyde. There is proof of this in the lines â€Å"I had taken a loathing to the man at first sight† and â€Å"gave me a look so ugly, it brought out the sweat on me like running†. Dr Jekylls idea was that everyone had two sides to them, a good side and an evil side, a side of joy and a side of despair; there is a Mr Hyde in all of us. This was not the only novel of this time that hinted at duality, there were a few of other main plays. Two examples are Deacon Broodie, and Markheim, which is a short story. In the Victorian times, most people had very high morals, and so immoral things were rarely mentioned or talked about. Also, sex is rarely talked about in the book because everyone had such high morals, and so sex is something which would not be written and so was kept away from the public eye. [PD2] Throughout the novel, figurative language is used in various forms. One of the forms used is personification. Personification is used in many ways to try to help the reader to relate to the book, characters, and objects in the book. Another way figurative language is used is in the form of â€Å"similes† by saying things like â€Å"You start a question, and it’s like starting a stone. You sit quietly on the top of a hill; and away the stone goes, starting others; and presently some bland old bird (the last you would have thought of) is knocked on the head in his own back garden and the family have to change their name. No, sir, I make it a rule of mine: the more it looks like Queer Street, the less I ask.†[PD3] The novel was written when the world was not very advanced medically. In the world today, we know of illnesses such as schizophrenia. It is thought that the novel was written about someone who had schizophrenia which would be treated with medication nowadays. jekyll and hyde were indeed the same person and Dr Jekyll didn’t really have â€Å"an evil side† to him, it was just because he had a split personality disorder.