Notable similarities between Sarah Penn of ‘Revolt of Mother’ and Grace Ansley of ‘Roman Fever’ Essay Example

Notable similarities between Sarah Penn of ‘Revolt of Mother’ and Grace Ansley of ‘Roman Fever’ Essay It is quite true that Sarah Penn and Grace Ansley come from contrasting social backgrounds and are separated in terms of place and period. Roman Fever is set at the turn of the 20th century and reflects the values and ethos of urban America at that time. Grace Ansley, though belonging to a particular historical era, cannot be said to typify all women of that era. The strongest proof of her uniqueness is obtained in comparison to her antagonist Alida Slade. Revolt of Mother, in contrast, is set in rural America. Its primary character, Sarah Penn, is a good representation of the homemakers of that generation. She shares the same problems that most women of her generation suffered, chief among them being male domination. While there are these undeniable differences in terms of their social mileau, the stories of the two women share many similarities. The rest of this essay will delve into these similarities. The most common characteristic between Sarah Penn and Grace Ansley is their strong will. Their stories were set at a time when women’s rights were muted and their self-expression undermined. Yet, in their own ways, the two women show daring and assertiveness. This is not to say that their opposition comes in the form of men. It is the prevailing social mores and prevalent patriarchal mindset that serve as their oppressors. For, in terms of the actual personnel, both men and women present the two women several challenges. The antagonist of Mrs. Ansley is her envious friend of many years Mrs. Slade. Despite the poly-amorous streak in Mr. Delphin, he is not the main oppressor to either of these women. Years earlier, when the two were young women, Mrs. Slade hatches a cunning plan to mislead Mrs. Ansley. Anticipating her fiance’s rendezvous with Ansley, Slade writes a forged letter of excuse on behalf of her fiance. The letter announces the cancellation of the rendezvous. B ut as fate would have it, the politeness of Mrs. Ansley in replying to this letter proves to be a blessing. Upon reading this reply, Mr. Delphin declares to meet her that evening. This fact is not privy to Mrs. Slade, who is smug on the belief that she sabotaged the prospects of a competitor to her fiance’s attention. It is often lamented that women in the 19th century suffered male domination. But the evidence of Roman Fever prompts how women were undermining their own cause. We will write a custom essay sample on Notable similarities between Sarah Penn of ‘Revolt of Mother’ and Grace Ansley of ‘Roman Fever’ specifically for you for only $16.38 $13.9/page Order now We will write a custom essay sample on Notable similarities between Sarah Penn of ‘Revolt of Mother’ and Grace Ansley of ‘Roman Fever’ specifically for you FOR ONLY $16.38 $13.9/page Hire Writer We will write a custom essay sample on Notable similarities between Sarah Penn of ‘Revolt of Mother’ and Grace Ansley of ‘Roman Fever’ specifically for you FOR ONLY $16.38 $13.9/page Hire Writer The strength of character of the two women can be learnt from other details as well. For example, despite attempts of deception on part of Mrs. Slade, Mrs. Ansley displays real strength of character. She spends no time fretting and regretting about having to lose Mr. Delphin. Instead, the consummation of their love bears her Barbara, who grows into a lovely young girl. As the final line of the story claims triumphantly, Barbara is indeed a symbol of Mrs. Ansley’s success. Despite not having Mr.Delphin to support her and Barbara, Mrs. Ansley does an exemplary job in raising her daughter. She must have surely felt the pain of social ostracization. Yet her tenacity and perseverance proves fruitful in the end. That fruition is in the healthy blossoming of young Barbara. Just like Mrs. Ansley emerges a successful woman in spite of her adversities, so does Mrs. Sarah Penn. In Mrs. Penn’s case, the adversity is not so much an individual as a whole social structure. Her husband , though not meaning to insult her, finds no qualms in being the sole decision maker in the family. In her long battle to alter the mindset of her husband, Mrs. Penn tries various methods of persuasion. But it eventually occurs to her that no amount of constructive dialogue or protest is going to help her meet her objective. This objective of Mrs. Penn is to build a bigger house for the family to live in. But, as Mary Wilkins Freeman skillfully portrays, women of the era faced numerous hurdles in realizing their interests. Mrs. Penn’s patient fight for self-determination comes to a climax when she finally decides to convert the barn into the house without informing her husband. This decisive act of hers towards the end of the story can be interpreted at multiple levels. The obvious interpretation is that it achieves a practical end, namely, finding a bigger abode for a growing family. But, more importantly, it is a symbolic victory for the long suffering wife in Mrs. Penn. While Mrs. Ansley and Mrs. Penn are obviously the heroes of their respective tales, there are other characters in the plot who serve as scaffolds for presenting their heroism. In the case of Revolt of Mother, it is the children of Mrs. Penn. It is her conversations with them about her preference for a bigger house that creates tension in the plot and takes it forward. Likewise, for Mrs. Ansley, it is the lengthy exchanges with Mrs. Slade, which serves a similar purpose. Although the two women were once childhood friends, their common interest in the same man had undermined their friendship. As adults with daughters of their own, their sense of possessiveness and self-interestedness only grows bigger in the intervening years. Yet it is Mrs. Ansley who comes across as the kinder and more generous of the two. She is not blatantly jealous of the happy married life of Mrs. Slade. Another commonality between Mrs. Penn and Mrs. Ansley is their relation to men. Mr. Adoniram and Mr. Delphin are the two significant men in their respective lives. Needless to say, in the both the cases, the men are either indifferent or plain absent to heed to their women’s concerns. Adoniram is a typical patriarchal figure who presumes his authority to be a natural fact of life. Author Freeman is careful not to portray Adoniram as the villain of a melodrama, for in truth he is not one. He comes across a simple person, one who carries an inflated ego as a result of his misplaced claim to authority. But the fragility and the lack of substance to his bloated ego is exposed the moment Sarah is able to find the â€Å"right besieging tools†. Having found his measure thus, she was able to dismantle his authority, his self-concept and also achieve her goal all in one stroke. Indeed, there is an interesting interpretation to the two selves of Adoniram – the one before a nd the one after Sarah’s bold revolt. These two selves are likened to the two houses of the family. The first is the congested and ill-equipped old house, and the second is the more expansive and better suited new house. Sarah Penn’s revolt, in this sense, is also an act of liberating her husband from his shackled mindset. Coming to Mr. Delphin, one cannot really blame him for being absent from Grace Ansley and Barbara. Grace knew all too well that such is going to be the case and willingly consented to have Barbara. Nevertheless, it is a sad fact, that the men in their lives had not been supportive to them. The stories of the two women share a few common symbols too. Just as The Revolt of Mother has its share of symbolism, so does Roman Fever. For Mrs. Ansley, the real victory is the successful rearing of her girl child Barbara. Indeed Barbara is the symbol of her victory, apart from being the joy and pride for Mrs. Ansley. Just as living quarters are used as symbols of personalities in Revolt of Mother, buildings used to symbolize themes in Roman Fever. For example, in Revolt of Mother the old house is contrasted to the newer and bigger structure of the barn. In a similar fashion, buildings such as the Palatine, Coliseum and the Forum are used to depict the nature of relationships between the characters. Since these are monuments already in ruins, they aptly symbolize the state of relationship between Mrs. Ansley and Mrs. Slade. The Coliseum is an apt metaphor, for it was the site of gladiatorial fights during the peak of the Roman Empire. As well, they represent their widowed status, which is a cause of deep pain for both of them.

Learn How to Solve an Entropy Change Problem

Learn How to Solve an Entropy Change Problem This example problem demonstrates how to examine the reactants and products to predict the sign of the change in entropy of a reaction. Knowing if the change in entropy should be positive or negative is a useful tool to check your work on problems involving changes in entropy. It is easy to lose a sign during thermochemistry homework problems. Entropy Problem Determine if the entropy change will be positive or negative for the following reactions:A) (NH4)2Cr2O7(s) → Cr2O3(s) 4 H2O(l) CO2(g)B) 2 H2(g) O2(g) → 2 H2O(g)C) PCl5 → PCl3 Cl2(g) Solution Entropy of a reaction refers to the positional probabilities for each reactant. An atom in gas phase has more options for position than the same atom in a solid phase. This is why gases have more entropy than solids.In reactions, the positional probabilities must be compared for all the reactants to the products produced.If the reaction involves only gases, the entropy is related to the total number of moles on either side of the reaction. A decrease in the number of moles on the product side means lower entropy. An increase in the number of moles on the product side means higher entropy.If the reaction involves multiple phases, the production of a gas typically increases the entropy much more than any increase in moles of a liquid or solid.Reaction A(NH4)2Cr2O7(s) → Cr2O3(s) 4 H2O(l) CO2(g)The reactant side contains only one mole where the product side has six moles produced. The was also a gas produced. The change in entropy will be positive.Reaction B2 H2(g) O2(g) â†⠀™ 2 H2O(g)There are 3 moles on the reactant side and only 2 on the product side. The change in entropy will be negative.Reaction CPCl5 → PCl3 Cl2(g)There are more moles on the product side than on the reactant side, therefore the change in entropy will be positive. Answer: Reactions A and C will have positive changes in entropy.Reaction B will have negative changes in entropy.

Yahoo Inc Statistics Project Example | Topics and Well Written Essays - 2500 words

Yahoo Inc - Statistics Project Example It is important to observe trends in the time series data in order to assess which model to apply when undertaking a forecast of data. In this regard, graphical presentations are usually recommended. Thus in this study, graphs are used to show the trends in the data gathered as has been recommended by other scholars for observing time series data (Anderson, et al., 2010). One of the objectives of this paper was to assess the measures of central tendency and measures of dispersion (spread). In this study, we use mean scores and median values to assess central tendency and standard deviation, minimum and maximum values to assess spread in the time series data. Therefore, this has been done for sales, costs, and profits of Yahoo Inc. for the period under review. These results are presented in table formats in Part II of this paper. There are many methods that can be used to forecast time series data such as the one in this study (Table 1). In our assessment of the task at hand and the l imitation of resources, we conducted the forecast using regression analysis technique which we found to be appropriate for this study. There are a number of statistical software available for conducting forecasts of time series data (Evans, 2003). These include but are not limited to Excel, SPSS, Stata, Eviews, R, and Minitab. Again, due to resource limitations and our assessment of the task at hand, we use the Excel software to conduct the forecasting exercise in the present study.

Market Research Essay Example | Topics and Well Written Essays - 500 words

Market Research - Essay Example eed that must be ignited within them to look good, feel good and be different from the rest of the men who do not use this product or any similar product in the first place and eventually feel left out when it comes to the manly appeal, the facial looks and so on. Thus market research for launching a new skin care product would first be understood by the proposition of clearly identifying the particular segments which will be hit upon (market segmentation) and then target the message in a way (market targeting) that the positioning and focus of the product does not mingle with the already present products within the market (market positioning). For this, it is significant to note that market research will touch upon areas like the exact age groups which would be catered to whilst understanding the product, the social class that it will take care of, the economic conditions which must be comprehended beforehand whilst discussing the determinants of the market research module and more than anything else what kind of people would actually be the end customers for the said product (qualitative research will be applicable here). (Baines, 2002) If there are any misunderstandings within the relevant target domains that the skin care products are just for women and men have got no relation with them; the same will be removed by this product, i.e., if it is the first mover within the market. The first mover would surely have his advantage nonetheless but then again its job will be cut out and there would be hard figures to achieve in a certain amount of time. Thus the skin care product would take care of market that will be segmented in terms of age groups, economic factions, social class regimes, the geographic locations and so on. The importance of this new skin care product towards the organization is immense because this will be the driver for the organization in the first place. The company’s financial and fiscal revenues would be dedicated solely on the skin

Structured essay on a comprehensive Marketing Plan to Promote YHA

Structured on a comprehensive Marketing Plan to Promote YHA Australia using UKs Back Packers as market target - Essay Example Cheaper flights and favourable exchange rates have encouraged the tremendous growth of this market with more than 400 000 backpackers expected to visit Australia in 2002. (Macbeth and Westerhausen, 2003) Backpackers hold special potential for regional Australia. Already, backpackers make up more than half of all international visitors and visitor nights in some parts of regional Australia. Their tendency to roam farther afield than other types of tourists is reflected in the fact that backpackers visited an average 10.6 regions in Australia during 1995-96, compared with 2.7 regions for all visitors. However although backpackers are visiting up to four times more of Australia than other types of tourists, large sections of regional Australia continue to be bypassed altogether The marketing strategy for backpacker tourism is composed of four strategies: promote 'quality' tourism based upon: maximizing income from tourism through a value-volume strategy (i.e. relatively lower growth in arrivals, but targeting higher-spending visitors); reducing seasonality; repositioning Australia's image as a destination, with greater emphasis on experiences linked to the island's environment and cultural heritage, marketing Australia's diverse population as a 'a mosaic of nature and culture, a whole, magical world concentrated in a small, warm and hospitable island in the Mediterranean at the crossroads of three continents, between West and East, that offers a multidimensional, high quality tourist experience. Tourism Australia has been active in this segment for a number of years, and is building on past experience to continue to develop it. Investing in this segment now will provide substantial returns in the future as the backpackers of today are likely to become the returning high-yield target markets of tomorrow. Backpackers area unique tourism segment. Their characteristics are as follows: there is an evident and strong social interaction among backpackers, the existence of backpacker enclaves, the relatively prolonged duration of most backpacker journeys compared to the conventional tourist trips), and the inviting traits of a classic anthropological subject, rites of passage. Parallel with the growth and expansion of the phenomenon itself, research into backpacker tourism has grown dramatically too, and a noteworthy share of that research has been conducted by means of ethnography, while a large share of the remainder display much influence from ethnographic methodology. The autho r has been part and parcel of this development as he, since 1990, in total has conducted more than two years of ethnographic fieldwork among backpackers and has published several papers on the ethnography of backpackers INTRODUCTION 'Travel and tourism is the largest industry in the world, accounting for 11.7 per cent of world GDP, 8 per cent of world export earnings, and 8 per cent of employment. This mobility affects almost everywhere, with the World Tourism Organization publishing tourism statistics for over 180 countries (WTO 2002). Almost no countries are not significant senders and receivers of visitors. Internationally there are over 700 million legal passenger arrivals each year (compared with 25 million in 1950) with a predicted 1 billion by

Supervised Image Classification Techniques

Supervised Image Classification Techniques Introduction In this chapter, a review of Web-Based GIS Technology and Satellite image classification techniques. Section 2.2 presents a review of Web-Based GIS Technology.in section 2.3 Satellite images classification techniques are reviewed.In section 2.4 presents the related work .section 2.5 presents uses of web based GIS applications in real world. Section 2.6 presents available commercial web GIS sites. Section 2.7 reviews the types of Geospatial Web Services (OGC) 2.3 Image Classification Image classification is a procedure to automatically categorize all pixels in an Image of a terrain into land cover classes. Normally, multispectral data are used to Perform the classification of the spectral pattern present within the data for each pixel is used as the numerical basis for categorization. This concept is dealt under the Broad subject, namely, Pattern Recognition. Spectral pattern recognition refers to the Family of classification procedures that utilizes this pixel-by-pixel spectral information as the basis for automated land cover classification. Spatial pattern recognition involves the categorization of image pixels on the basis of the spatial relationship with pixels surrounding them. Image classification techniques are grouped into two types, namely supervised and unsupervised[1]. The classification process may also include features, Such as, land surface elevation and the soil type that are not derived from the image. Two categories of classification are contain ed different types of techniques can be seen in fig Fig. 1 Flow Chart showing Image Classification[1] 2.3 Basic steps to apply Supervised Classification A supervised classification algorithm requires a training sample for each class, that is, a collection of data points known to have come from the class of interest. The classification is thus based on how close a point to be classified is to each training sample. We shall not attempt to define the word close other than to say that both Geometric and statistical distance measures are used in practical pattern recognition algorithms. The training samples are representative of the known classes of interest to the analyst. Classification methods that relay on use of training patterns are called supervised classification methods[1]. The three basic steps (Fig. 2) involved in a typical supervised classification procedure are as follows: Fig. 2. Basic steps supervised classification [1] (i) Training stage: The analyst identifies representative training areas and develops numerical descriptions of the spectral signatures of each land cover type of interest in the scene. (ii) The classification stag(Decision Rule)e: Each pixel in the image data set IS categorized into the land cover class it most closely resembles. If the pixel is insufficiently similar to any training data set it is usually labeled Unknown. (iii) The output stage: The results may be used in a number of different ways. Three typical forms of output products are thematic maps, tables and digital data files which become input data for GIS. The output of image classification becomes input for GIS for spatial analysis of the terrain. Fig. 2 depicts the flow of operations to be performed during image classification of remotely sensed data of an area which ultimately leads to create database as an input for GIS. Plate 6 shows the land use/ land cover color coded image, which is an output of image 2.3.1 Decision Rule in image classiffication After the signatures are defined, the pixels of the image are sorted into classes based on the signatures by use of a classification decision rule. The decision rule is a mathematical algorithm that, using data contained in the signature, performs the actual sorting of pixels into distinct class values[2]. There are a number of powerful supervised classifiers based on the statistics, which are commonly, used for various applications. A few of them are a minimum distance to means method, average distance method, parallelepiped method, maximum likelihood method, modified maximum likelihood method, Baysians method, decision tree classification, and discriminant functions. Decision Rule can be classified into two types: 1- Parametric Decision Rule: A parametric decision rule is trained by the parametric signatures. These signatures are defined by the mean vector and covariance matrix for the data file values of the pixels in the signatures. When a parametric decision rule is used, every pixel is assigned to a class since the parametric decision space is continuous[3] 2-Nonparametric Decision Rule A nonparametric decision rule is not based on statistics; therefore, it is independent of the properties of the data. If a pixel is located within the boundary of a nonparametric signature, then this decision rule assigns the pixel to the signatures class. Basically, a nonparametric decision rule determines whether or not the pixel is located inside of nonparametric signature boundary[3] . 2.3.2 supervised algorithm for image classiffication The principles and working algorithms of all these supervised classifiers are derived as follow : Parallelepiped Classification Parallelepiped classification, sometimes also known as box decision rule, or level-slice procedures, are based on the ranges of values within the training data to define regions within a multidimensional data space. The spectral values of unclassified pixels are projected into data space; those that fall within the regions defined by the training data are assigned to the appropriate categories [1]. In this method a parallelepiped-like (i.e., hyper-rectangle) subspace is defined for each class. Using the training data for each class the limits of the parallelepiped subspace can be defined either by the minimum and maximum pixel values in the given class, or by a certain number of standard deviations on either side of the mean of the training data for the given class . The pixels lying inside the parallelepipeds are tagged to this class. Figure depicts this criterion in cases of two-dimensional feature space[4]. Fig. 3. Implementation of the parallelepiped classification method for three classes using two spectral bands, after[4]. Minimum Distance Classification for supervised classification, these groups are formed by values of pixels within the training fields defined by the analyst.Each cluster can be represented by its centroid, often defined as its mean value. As unassigned pixels are considered for assignment to one of the several classes, the multidimensional distance to each cluster centroid is calculated, and the pixel is then assigned to the closest cluster. Thus the classification proceeds by always using the minimum distance from a given pixel to a cluster centroid defined by the training data as the spectral manifestation of an informational class. Minimum distance classifiers are direct in concept and in implementation but are not widely used in remote sensing work. In its simplest form, minimum distance classification is not always accurate; there is no provision for accommodating differences in variability of classes, and some classes may overlap at their edges. It is possible to devise more sophisticated versions of the basi c approach just outlined by using different distance measures and different methods of defining cluster centroids.[1] Fig. 4. Minimum distance classifier[1] The Euclidean distance is the most common distance metric used in low dimensional data sets. It is also known as the L2 norm. The Euclidean distance is the usual manner in which distance is measured in real world. In this sense, Manhattan distance tends to be more robust to noisy data. Euclidean distance = (1) Where x and y are m-dimensional vectors and denoted by x = (x1, x2, x3 xm) and y = (y1, y2, y3 ym) represent the m attribute values of two classes. [5]. While Euclidean metric is useful in low dimensions, it doesnt work well in high dimensions and for categorical variables. Mahalanobis Distance Mahalanobis Distance is similar to Minimum Distance, except that the covariance matrix is used in the equation. Mahalanobis distance is a well-known statistical distance function. Here, a measure of variability can be incorporated into the distance metric directly. Mahalanobis distance is a distance measure between two points in the space defined by two or more correlated variables. That is to say, Mahalanobis distance takes the correlations within a data set between the variable into consideration. If there are two non-correlated variables, the Mahalanobis distance between the points of the variable in a 2D scatter plot is same as Euclidean distance. In mathematical terms, the Mahalanobis distance is equal to the Euclidean distance when the covariance matrix is the unit matrix. This is exactly the case then if the two columns of the standardized data matrix are orthogonal. The Mahalanobis distance depends on the covariance matrix of the attribute and adequately accounts for the corr elations. Here, the covariance matrix is utilized to correct the effects of cross-covariance between two components of random variable[6, 7]. D=(X-Mc)T (COVc)-1(X-Mc) ( 2) where D = Mahalanobis Distance, c = a particular class, X = measurement vector of the candidate pixel Mc = mean vector of the signature of class c, Covc = covariance matrix of the pixels in the signature of class c, Covc-1 = inverse of Covc, T = transposition function[3]. Maximum Likelihood Classification In nature the classes that we classify exhibit natural variation in their spectral patterns. Further variability is added by the effects of haze, topographic shadowing, system noise, and the effects of mixed pixels. As a result, remote sensing images seldom record spectrally pure classes; more typically, they display a range of brightnesss in each band. The classification strategies considered thus far do not consider variation that may be present within spectral categories and do not address problems that arise when frequency distributions of spectral values from separate categories overlap. The maximum likelihood (ML) procedure is the most common supervised method used with remote sensing. It can be described as a statistical approach to pattern recognition where the probability of a pixel belonging to each of a predefined set of classes is calculated; hence the pixel is assigned to the class with the highest probability [4]MLC is based on the Bayesian probability formula. Bayes Classification: The MLC decision rule is based on a normalized (Gaussian) estimate of the probability density function of each class [8]. Hence, under this assumption and using the mean vector along with the covariance matrix, the distribution of a category response pattern can be completely described [9]. Given these parameters, the statistical probability of a given pixel value can be computed for being a member of a particular class. The pixel would be assigned to the class with highest probability value or be labelled unknown if the probability values are all below a threshold set by the user [10]. Let the spectral classes for an image be represented by à Ã¢â‚¬ °i, i = 1, . . . M Where, M is the total number of classes. In order to determine the class to which a pixel vector x belongs; the conditional probabilities of interest should be followed. P( à Ã¢â‚¬ °i|x), i = 1, . . . M The measurement vector x is a column of Digital Numbers (DN) values for the pixel, where its dimension depends on the number of input bands. This vector describes the pixel as a point in multispectral space with co-ordinates defined by the DNs (Figure 2-20). Fig. 4.Feature space and how a feature vector is plotted in the feature space [9] The probability p(à Ã¢â‚¬ °i |x) gives the likelihood that the correct class is à Ã¢â‚¬ °i for a pixel at position x. Classification is performed according to: x à ¢Ã‹â€ Ã‹â€  à Ã¢â‚¬ °i if p à Ã¢â‚¬ °i |x > p à Ã¢â‚¬ °j |x) for all j à ¢Ã¢â‚¬ °Ã‚   i3 i.e., the pixel at x belongs to class à Ã¢â‚¬ °i if p(à Ã¢â‚¬ °i|x) is the largest. This general approach is called Bayes classification which works as an intuitive decision for the Maximum Likelihood Classifier method [11]. From this discussion one may ask how can the available p(x|à Ã¢â‚¬ °i) can be related from the training data set, to the desired p(à Ã¢â‚¬ °i|x) and the answer is again found in Bayes theorem [12]. From this discussion one may ask how can the available p(x|à Ã¢â‚¬ °i) can be related from the training data set, to the desired p(à Ã¢â‚¬ °i|x) and the answer is again found in Bayes theorem [12]. p (à Ã¢â‚¬ °i|x)= p (x|à Ã¢â‚¬ °i) p (à Ã¢â‚¬ °i )/p(x) 4 Where p(à Ã¢â‚¬ °i ) is the probability that class à Ã¢â‚¬ °i occurs in the image and also called a priori or prior probabilities. And p(x) is the probability of finding a pixel from any class at location x. Maximum Likelihood decision rule is based on the probability that a pixel belongs to a particular class. The basic equation assumes that these probabilities are equal for all classes, and that the input bands have normal distributions as in [13] D = ln(ac)-[0.5ln(|Covc|)]-[0.5(X-Mc)T(Cov-1)(X-Mc)] 6 Where: D = weighted distance (likelihood),c = a particular class,X = measurement vector of the candidate pixel, Mc =mean vector of the sample of class c,ac =percent probability that any candidate pixel is a member ofclass c,(Defaults to 1.0, or is entered from a priori knowledge),Covc = covariance matrix of the pixels in the sample of class c,|Covc| = determinant of Covariance (matrix algebra),Covc-1 = inverse of Covariance (matrix algebra) ln = natural logarithm function = transposition function (matrix algebra). 4- Comparison supervised classification techniques: One of the most important keys to classify land use or land cover using suitable techniques the table showed advantages and disadvantages of each techniques [3] : techniques advantage disadvantage Parallelepiped Fast and simple, calculations are made, thus cutting processing Not dependent on normal distributions. Since parallelepipeds have corners, pixels that are actually quite far, spectrally, from the mean of the signature may be classified Minimum Distance Classification Since every pixel is spectrally closer to either one sample mean or another, there are no unclassified pixels. Fastest decision rule to compute, except for parallelepiped Pixels that should be unclassified,, this problem is alleviated by thresholding out the pixels that are farthest from the means of their classes. Does not consider class variability Mahalanobis Distance Takes the variability of classes into account, unlike Minimum Distance or Parallelepiped Tends to overclassify signatures with relatively large values in the covariance matrix. Slower to compute than Parallelepiped or Minimum Distance Maximum Likelihood Most accurate of the classifiers In classification. Takes the variability of classes into account by using the covariance matrix, as does Mahalanobis Distance An extensive equation that takes a long time to compute Maximum Likelihood is parametric, meaning that it relies heavily on anormal distribution of the data in each input band 5- accuracy assessment No classification is complete until its accuracy has been assessed [10]In this context the accuracy means the level of agreement between labels assigned by the classifier and class allocation on the ground collected by the user as test data. To research valid conclusions about maps accuracy from some samples of the map the sample must be selected without bias. Failure to meet these important criteria affects the validity of any further analysis performed using the data because the resulting error matrix may over- or under- estimate the true accuracy. The sampling schemes well determine the distribution of samples across the land scape which will significantly affect accuracy assessment costs [14] When performing accuracy assessment for the whole classified image, the known reference data should be another set of data. Different from the set that is used for training the classifier .If training samples as the reference data are used then the result of the accuracy assessment only indicates how the training samples are classified, but does not indicate how the classifier performs elsewhere in scene [10]. the following are two methods commonly used to do the accuracy assessment derived from table . 1-the Error matrix Table 1.Error matrix[15] Error matrix (table1 ) is square ,with the same number of information classes that will be assessed as the row and column. Numbers in rows are the classification result and numbers in column are ref-erence data (ground truth ).in this square elements along the main diagonal are pixels that are correctly classified. Error matrix is very effective way to represent map accuracy in that individual accuracies of each category are plainly descried along with both the error of commission and error of omission. Error of commission is defined as including an area into acatogary when it does not belong to that category. Error of omission is defined as excluding that area from the catogary in which it truly does belong. Every error is an omission from correct category and commission to a wrong category. With error matrix error of omission and commission can be shown clearly and also several accuracy indexes such as overall accuracy, users accuracy and producers accuracy can be assessed .the fol lowing is detailed description about the three accuracy indexes and their calculation method overall accuracy Overall accuracy is the portion of all reference pixels, which are classified correctly (in the scene) that assignment of the classifications and of the reference classification agree).it is computed by dividing the total number of correctly classified pixels (the sum of the elements along the main diagonal) by the total number of reference pixels. According to the error matrix above the overall accuracy can be calculated as the following: OA == Overall accuracy is Avery coarse measurement. It gives no information about what classes are classified with good accuracy. producers accuracy producer accuracy estimates the probability that a pixel, which is of class I in the reference classification is correctly classified . It is estimate with the reference pixels of class I divided by the pixels where classification and reference classification agree in class I . Given the error matrix above, the producers accuracy can be calculated using the following equation: PA (class I) = Producer accuracy tells how well the classification agrees with reference classification 2.3 users accuracy Users accuracy is estimated by dividing the number of pixels of the classification results for class I with number of pixels that agree with the reference data in class I.it can be calculated as : UA(class I)= Users accuracy predicts the probability that a pixel classified as class I is actually belonging to class I. 2-kappa statistics The kappa analysis is discrete multivariate techniques used in accuracy assessment for statistically determining if one error matrix is significantly different than another (bishop).the result of performing of kappa analysis is KHAT statistics (actually ,an estimate of kappa),which is an- other measure of agreement or accuracy this measure of agreement is based on the difference between the actual agreement in the error matrix(i.e the agreement between the remotely sensed classification and the reference data as indicated by major diagonal) and the chance agreement, which is indicated by the row and column totals(i.e marginal)[16] A detailed comparison between two data sets, one with near-infrared and three visible and the other with the full 8-bands, was made to emphasize the important role of the new bands for improving the separability measurement and the final classification results [17]

Murakami as an Existential Writer Essay -- Philosophy, Writing

Existentialism is a 20th century philosophy and school of literature that holds that life is meaningless and chaotic, and any abstract theories about it are useless. All that exists is the world of phenomena as perceived by our senses. Whatever metaphysical concept that lies behind this world is not only impossible to know and understand, but also holds no significant value. The only choice we have to make in life is to accept this world with a kind of determined joy, to discipline ourselves, and to defy the emptiness and the chaos by finding our own meaning in life (â€Å"Friedrich Nietzsche Part 4†). Although Haruki Murakami does not directly express any existential views in What I Talk about When I Talk about Running and Norwegian Wood, he is a quintessential existential writer because so much of existentialism involves the working out of private dilemmas. There is much focus on introversion in existentialism, and it can be seen in the lives of Murakami’s chara cters. In What I Talk About When I Talk About Running, Murakami was facing the dilemma of participating in a 62-mile ultramarathon that took place every June at Lake Saroma in Hokkaido, Japan (104). According to Murakami, â€Å"The runners run around the shores of Lake Saroma, which faces the Sea of Okhotsk. Only once you actually run the course do you realize how ridiculously huge Lake Saroma is† (105). The weather gradually changed from being freezing to being too warm for heavy clothes during the ultramarathon (105). While Murakami was running, he began feeling intense pain in different parts of his body (109). Even so, he felt very happy upon reaching the finish line, not so much pride as a sense of completion (115). Through running, Murakami finds his own meaning... ... Through perseverance, we overcome obstacles and find happiness in this chaotic world of ours. We find our own reasons to live and we choose to hold our own values. All of these things are tenets of existentialism. There is no purpose in life but what we make for ourselves. Works Cited "Friedrich Nietzsche Part 4 - YouTube." YouTube - Broadcast Yourself. Web. 25 Sept. 2011. . Murakami, Haruki. What I Talk About When I Talk About Running. New York: Vintage, 2007. Print. Murakami, Haruki. Norwegian Wood. New York: Vintage International, 1987. Print. "Island of Freedom - Sà ¸ren Kierkegaard." RobertHSarkissian.com. Web. 27 Nov. 2011. . Murakami, Haruki, Alfred Birnbaum, and Jay Rubin. The Elephant Vanishes: Stories. (TEV)New York: Knopf, 1993. Print.

Critically Evaluate the Management Model of Baumol

Under the traditional economic understanding, it is always assumed that profit maximization is treated as the main goal or objective for businesses, subject to perfect knowledge, single entity and rational logic. However, as illustrated by the principal-agency problem, managers do not usually make rational decision entirely like owners who take company interest as their sole basis for their decisions. Past examples have shown that managers do take their own personal goals and satisfactions as consideration in their decision-making. In addition, information gathering is not always perfect as managers do make decision by relying solely on the implicit knowledge gained from past experiences, without referencing to the macro-economic environment and the current market changes. Combining all these factors, it is therefore understandable that businesses do not always work toward profit maximization, at least in the short term, and other objectives like financial objective, market share, executive power, etc. do involve in business decision making. However, as pointed out by various academics (Baumol, 1962; Marris, 1964; Williamson, 1963), profit maximization does not always serve as the only correct objective for a firm, especially at various phrases of the business on a timeline scale. A point-in-hand is Baumol model. As an alternative to profit maximization model, Baumol model works on the correlation between price and output decision with the objective of maximizing sales revenue, subjected to minimum profit constraint by shareholders. In profit maximization model, profit is maximized at the output where Marginal Revenue (MR) is equaled to Marginal Cost (MC) whereas Baumol Model emphasizes on maximizing sales revenue (TR) and may miss the MC = MR point to achieve its goal. This model argues that businesses try to maximize sales revenues rather than profits with the possible motives such as growing or sustaining market share, to fill up spare capacity, discourage new entrants, management performance and etc. In addition, Baumol model provides a platform to understand some of the pricing strategies adopted by certain industries, which usually share common characteristics of having huge sunk cost and low variable cost. In such industries where fixed cost or sunk cost takes up a huge part of the total cost, producing a single unit and its maximum allowable output (without expanding its capacity in the short term) does not have any significant impact to the total cost. In such instances, profit maximization model is neither practical nor feasible as a focus of the model relies on seeking the output point where MC=MR. In the case of Walt Disney, the operational cost does not differ much whether there is one patronage or maximum allowable patronages as a theme park has to be fully functional during its operation hours, which render the MC at zero or near zero level. The objective of the company to seek sales revenue maximization for the day rather than focusing its effort to achieve the output point where MC=MR to maximize its profit does make sense. This explains the two-part pricing strategy adopted by Walt Disney where a fix initialization fee per entry is charged and allows the patronage to have as many free rides as they wish. Another example is the telecommunication industry, where the initial investment/fixed cost (for example launching the satellite and setting up the infrastructure) is huge, and the variable cost per call is insignificant to the total cost. In such industry, firms will focus on maximizing sales revenue (with constraints to maximum capacity/output) by using strategies like price discrimination strategy. In this strategy, the firms charge a different unit price to peak and off-peak hours, as there is plenty of spare capacity at off-peak hours. Since MC of output is low, any additional revenue that can be generated from this surplus capacity will be profit to the firms. As such instance, it is rational for telecommunication industrial to adopt sales maximization model like Baumol. In addition, as short term capacity is always constraint and limited, telecommunication industries would not want to experience loss sales due to their inability to meet customer demand, especially during peak hour usage. As service providers, consistent and frequent service failure could prove to be fatal in term of their survival and their long-term brand reputation. Therefore it would make sense for telecommunication firms to divert the peak-hours traffic into non-peak hours by using a price discrimination strategy which segments the users based upon their willingness and abilities to pay. For instance, business-users are willing to pay higher price for peak-hours usage due to their inelastic demand whereas in contrast, leisure-users’ demand is elastic and are willing to make call during off-peak hours in return for lower price. By adopting the price discrimination strategy, telecommunication firms are able to maximize sales revenue during peak and off-peak hours by balancing the air-time traffic based upon different market segment of users. At this point, it is also noticeable that one of the characteristics of Baumol firms lies in the perishable products/services offered which cannot be inventoried. The loss sale of the day on the unutilized capacity/outputs is an opportunity cost to the firms. Baumol model is not only applicable to huge/large corporations, but also to small retailers like bakery shop or wet market, which explains the reason why some bakery shops offer a special discount one hour before the shop closed to maximize the revenue. The rationales applied similarly to the low cost carriers (LCC) where price discrimination is used as a strategy to maximize revenue. LCCs sell a cheaper price to early booking passengers and a higher price for last minute passengers to increase the revenue. LCCs used the existence of multiple segments to serve and the opportunity to utilize surplus capacities to generate additional revenue. The adoption of sales revenue maximization model is also used as an effective way of securing additional market share within a regulated market with limited players where market dominance is vital. In related to pricing, add-on product/services like travel insurance, priority boarding and choosing-a-seat are used as bundled offering to the customer to gain extra profit. Firms are willing to earn a smaller profit if it means that they are able to gain a competitive advantage from their rival firms. As an illustration, Fitness club is a good example to elaborate how Baumol model is applied through adopting different pricing strategies. The reasons behind Fitness Club in adopting Baumol model include penetration to new market segments, retaining existing customer and to fill up spare capacity. True Fitness, which is a chained fitness centre, is effectively using Baumol model by offering different pricing strategies to capture different market egments, for instance, offering monthly fees to uncertain-customer and yearly/lifetime membership to certain-customer. Two types of pricing strategies are used by True Fitness to maximum its sales revenue, which are:- i)Two part pricing ( lifetime membership) The company offers a lifetime membership at ? 1. 5K as one-off payment and charges a minimal price of ? 20 yearly as administration/subscription fee. By payi ng a lifetime membership fees as fixed price, the customers are able to enjoy the facilities for life for as low as ? . 67 per month, which no other rivals is able to compete with this low price. As per other industries discussed above, fitness clubs have the similar characteristic like high initial set up cost and low marginal cost to adopt the Baumol model. The company charges an upfront fee to gain maximum consumer surplus and utilizes the yearly subscription fees, which is equaled to the marginal cost/average variable cost, to cover its yearly running costs. In addition, in order to adopt the two-part pricing strategy to maximize sales revenue, the company needs to have a minimum output (also known as critical mass), so that the full consumer surplus can be derived from the fixed fees. For example, if the yearly running cost (without considering the depreciation cost of the initial set up) is ? 200,000, in order to offer a yearly subscription fee of ? 20, the company needs to have a minimum membership of 10,000 in order to reduce the average variable cost/marginal cost to this level. By adopting Baumol model, which gives a higher output with lower price, this is achievable. This pricing strategy is also applicable to other chained-companies where the firms can derive the maximum profit from the fixed fee and use it as capital/investment to set up a new chain store. At the same time, individual chain store is able to run by itself from the revenue derived from the minimal pricing. ii)Price discrimination ( monthly membership vs. lifetime membership) True Fitness segments their customers into certain and uncertain customer by ffering different pricing to monthly and lifetime membership. The club is willing to offer a lower price to customers who are willing to commit, in comparison to uncertain customers. As illustrated in earlier examples, fitness clubs need to fill up spare capacities as any unutilized capacity carried an opportunity cost. With customers’ commitment, they are able to secure their stability in term of both volume and sales revenue. For thos e uncertain customers, the company charges a higher price, which customer willing to pay due to the flexibility and short-term commitment. From the above illustrations, it is apparent that a key characteristic of the Baumol model hinges on the elasticity of demand. As shown, Baumol model uses pricing strategies as a mean to achieve revenue maximization, and is therefore heavily dependent on the price elasticity to achieve the objective of the model If the demand is inelastic, Baumol model will not work as the demand of the product/services will not increase proportionately and therefore the sales revenue will not maximize from the reduction in price. In addition, advertising effect has not been considered in the above examples, which is a common tactic used to increase the inelasticity of demand. The psychological effect of advertising has been proven effective in occupying the mindset of consumers through brand image building, which increase the affiliation of the consumers to certain products/services, thus the inelasticity of the demand. The pointers from the last paragraph are well-illustrated by the Memo 1 example in Baye text book. In this example, it is shown that the price change does not correlate with the demand. By reducing the price from the current, $10. 50 to $10, the subscribers drops from 881 subscribers to 842, causing the revenue to drop from $9251 to $8240 and therefore a profit drop of $614. 5. (Appendix 1). In reverse, the firm should increase the price to $11. 5 to maximize the revenue at $11282 and a price of $12. 5 to maximize the profit at $4734. One of the reasons is due to the advertising and promotional effort from the company which increases the inelasticity of the demand. Secondly, since a loyal group of subscribers has already been amassed, STARZ network functions more as an add-on product to the existing subscribers. The combination of these two factors explained the reason why sales revenues and profit actually increases with price increases. In addition, from the data on STARZ network (Appendix 1), it is apparent that STARZ network does not share the same characteristics of high fixed cost or excess capacity to apply Baumol model. Instead, it seems like advertising or bundled pricing works better for STARZ network rather then price reduction. Further to the points above, Baumol model players are highly susceptible to the price reaction from their rivals, which could easily result in a price-war especially in an oligopolistic market. The existence of a floor triggering price in Baumol model constrained the players from lowering the price too much which will defeat the purpose of revenue maximization. Thus, it is highly unfavorable for Baumol model players to induce a price-cutting reaction from their rivals when they attempt to lower the price. This explained why certain Baumol model players used â€Å"noise† as disguise to their rivals when lowering the price to achieve revenue maximization. To summarize, long-term profit pursuance remains as the ultimate objective for any business. However, due to dissimilar characteristics of different industries, there are various models that can be used to achieve this long-term objective, which explains why certain firms are willing to sacrifice profit today in exchange for profit tomorrow. As illustrated through various examples in this assignment, the application of the correct model for the right industries and at the correct phrase of the company life-cycle becomes an even more important decision for managers to make. With the understanding and knowledge gained through the detailed analysis and critique of Baumol model, an useful insight to the economic rationale adopted by various industries, like Walt Disney, LCC and telecommunication firms is achieved. Bibliography Mercuro, N. , Haralambos, S. Gerald, W. , 1992. Ownership Structure, Value of the Firm and the Bargaining Power of the Manager. Southern Economic Journal, 59(2), pp. 273-83. Baumol, W. J. , 1996. Prediction and the logic of the Averages Variable Cost Test. Journal of Law and Economics, 39(1), pp. 49 – 72. McNutt, P. A. , 2008, â€Å"Signalling, Strategy & Management Type†, Available at: http://www. patrickmcnutt. com/docs/PatrickMcNutt. com_ebook [Accesses 20 Jan 2009]. Baye, M. R. , 2009 . Managerial Economics and Business Strategy. International ed. New York: McGraw-Hill. Conway, L. L. & Craycraft, J. L. , 1974. Sales Maximisation and Oligopoly: A Case Study. Journal of Industrial Economics, 23(2), pp. 81-95. Armstrong, M. & Vickers, J. , 2001. Competitive Price Discrimination. The Rand Journal of Economics, 32(4), pp. 579-605. Oi, W. Y. , 1971. A Disneyland Dilemma: Two-Part Tariffs for a Mickey Mouse Monopoly. The Journal of Economics, 85(1), pp. 77-96. McNutt, P. A.. Management Objectives and Stakeholder Value (Study Guide Unit 1).

L ocalization of functions in Psychology is a theo Essays

L ocalization of functions in Psychology is a theo Essays L ocalization of functions in Psychology is a theory that refers to the idea that different parts of the brain are responsible for specific behaviors, or that certain functions arelocalizedto certain areas in the brain. A study done by Robert Health (1950) clearly explained the theory of localization in which he tested the localization for pleasure. Another study demonstrated by James Olds (1950) in which he investigated the effect of stimulating the nucleus accumbens for rats. Robert health's aim from his study was to investigate the role of the nucleus accumbens (pleasure center of the brain).To do this, he used depressed patients . Depression is a great way to investigate this study as it is a common mental disorder that causes people to experience depressed mood, loss of interest or pleasure . The participants had electrodes attached to their head so that when they press the button it would electrically stimulates their nucleus accumbens and they would receive pleasure. The results indicated that over three hour session ,one participant known as b19 stimulated himself 1500 times. He experienced extreme euphoria and elation to a point where he had to be disconnected. A very similar study was conducted by James Olds but in his case, his aim was to understand the effect of activating the nucleus accumbens on rats. Olds began his procedure by having rats press on a lever in which it electrically stimulated their nucleus accumbens . The results indicated that rats were willing to walk across electrified grids to reach the pleasure lever and they were even willing to cross the electric rid and they preferred the stimulation over water and drinks. In conclusion, Robert health and James Old demonstrated localization of brain function since they found that pleasure is localizing nucleus accumbence . Although Health experimented his study on humans where as Olds experimented it on rats but they both were able to come up with the same conclusion. Evolutionis the change in the heritable characteristics of biological populations (species) over successive generation.Evolutionary processes give rise to biodiversity at every level of biological organisation , including the levels of species and i ndividual organisms and it mainly relies on the process of natural selection which basically means that organisms with the best characteristics and are adapted to their environment can pass down their genes to their offspring where as the ones that are not well adapted do not pass their genes to their offspring. Over time this results in significant changes in species. A behavior that can be explained by evolution is attraction. There are evolutionary explanation on why we find some people attractive and based on evolutionary benefits which yield for better offspring. This essay is going to discuss the theory of attraction with reference to Wedekind experiment that was conducted in 1995 Weekind's aim for his experiment was to investigate the role of genes (MHC) r elated to immune system in mate selection . MHC genes have a really huge benefits on the immune system as it helps it to identify foreign substances in the body and by having a huge amount of MHC this can lead for a better identification for harmful substances so by having a diverse MHC this can lead for a better immune system for the child therefore humans must have envolve system that can recognize potential mate s with different MHC genes,so t hey can reproduce together offspring with stronger immune systems. For this experiment they used 49 women and 44 men with a wide range of MHC genes. Each man received a clean T shirt and was asked to wear it for 2 nights, as each man was given odour free soup/aftershave and were asked to ensure they remain odour neutral (in order to induce the stronger body odour ) and they were forbidden to eat spicy food. After th e men returned the t shirts, each t shirt was placed in a plastic lined cardboard box with the sniffing hole on top. The women were returned to the study when they were in the middle of the cycle because of their smell was the strongest at that point and they received set of 7 boxes in which 3 of the

The Blackstone Commentaries and Womens Right

The Blackstone Commentaries and Women's Right In the 19th century, American and British womens rights- or lack of them- depended heavily on the commentaries of William Blackstone which defined a married woman and man as one person under the law. Heres what William Blackstone wrote in 1765: By marriage, the husband and wife are one person in law: that is, the very being or legal existence of the woman is suspended during the marriage, or at least is incorporated and consolidated into that of the husband; under whose wing, protection, and cover, she performs every thing; and is therefore called in our law-French a feme-covert, foemina viro co-operta; is said to be covert-baron, or under the protection and influence of her husband, her baron, or lord; and her condition during her marriage is called her coverture. Upon this principle, of a union of person in husband and wife, depend almost all the legal rights, duties, and disabilities, that either of them acquire by the marriage. I speak not at present of the rights of property, but of such as are merely personal. For this reason, a man cannot grant anything to his wife, or enter into covenant with her: for the grant would be to suppose her separate existence; and to covenant with her, would be only to covenant with hims elf: and therefore it is also generally true, that all compacts made between husband and wife, when single, are voided by the intermarriage. A woman indeed may be attorney for her husband; for that implies no separation from, but is rather a representation of, her lord. And a husband may also bequeath any thing to his wife by will; for that cannot take effect till the coverture is determined by his death. The husband is bound to provide his wife with necessaries by law, as much as himself; and, if she contracts debts for them, he is obliged to pay them; but for anything besides necessaries he is not chargeable. Also if a wife elopes, and lives with another man, the husband is not chargeable even for necessaries; at least if the person who furnishes them is sufficiently apprized of her elopement. If the wife be indebted before marriage, the husband is bound afterwards to pay the debt; for he has adopted her and her circumstances together. If the wife be injured in her person or her p roperty, she can bring no action for redress without her husbands concurrence, and in his name, as well as her own: neither can she be sued without making the husband a defendant. There is indeed one case where the wife shall sue and be sued as a feme sole, viz. where the husband has abjured the realm, or is banished, for then he is dead in law; and the husband being thus disabled to sue for or defend the wife, it would be most unreasonable if she had no remedy, or could make no defence at all. In criminal prosecutions, it is true, the wife may be indicted and punished separately; for the union is only a civil union. But in trials of any sort they are not allowed to be evidence for, or against, each other: partly because it is impossible their testimony should be indifferent, but principally because of the union of person; and therefore, if they were admitted to be witness for each other, they would contradict one maxim of law, nemo in propria causa testis esse debet; and if against each other, they would contradict another maxim, nemo tenetur seipsum accusare. But, where the offence is directly against the person of the wife, this rule has been usually dispensed with; and therefore, by statute 3 Hen. VII, c. 2, in case a woman be forcibly taken away, and married, she may be a witness against such her husband, in order to convict him of felony. For in this case she can with no propriety be reckoned his wife; because a main ingredient, her consent, was wanting to the contract: and also there is another maxim of law, that no man shall take advantage of his own wrong; which the ravisher here would do, if, by forcibly marrying a woman, he could prevent her from being a witness, who is perhaps the only witness to that very fact. In the civil law the husband and the wife are considered as two distinct persons, and may have separate estates, contracts, debts, and injuries; and therefore in our ecclesiastical courts, a woman may sue and be sued without her husband. But though our law in general considers man and wife as one person, yet there are some instances in which she is separately considered; as inferior to him, and acting by his compulsion. And therefore any deeds executed, and acts done, by her, during her coverture, are void; except it be a fine, or the like manner of record, in which case she must be solely and secretly examined, to learn if her act be voluntary. She cannot by will devise lands to her husband, unless under special circumstances; for at the time of making it she is supposed to be under his coercion. And in some felonies, and other inferior crimes, committed by her through constraint of her husband, the law excuses her: but this extends not to treason or murder. The husband also, by the old law, might give his wife moderate correction. For, as he is to answer for her misbehaviour, the law thought it reasonable to intrust him with this power of restraining her, by domestic chastisement, in the same moderation that a man is allowed to correct his apprentices or children; for whom the master or parent is also liable in some cases to answer. But this power of correction was confined within reasonable bounds, and the husband was prohibited from using any violence to his wife, aliter quam ad virum, ex causa regiminis et castigationis uxoris suae, licite et rationabiliter pertinet. The civil law gave the husband the same, or a larger, authority over his wife: allowing him, for some misdemeanors, flagellis et fustibus acriter verberare uxorem; for others, only modicam castigationem adhibere. But with us, in the politer reign of Charles the second, this power of correction began to be doubted; and a wife may now have security of the peace against he r husband; or, in return, a husband against his wife. Yet the lower rank of people, who were always fond of the old common law, still claim and exert their ancient privilege: and the courts of law will still permit a husband to restrain a wife of her liberty, in the case of any gross misbehaviour. These are the chief legal effects of marriage during the coverture; upon which we may observe, that even the disabilities which the wife lies under are for the most part intended for her protection and benefit: so great a favourite is the female sex of the laws of England. Source ï » ¿William Blackstone. Commentaries on the Laws of England. Vol, 1 (1765), pages 442-445.

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

Philo - Essay Example Narrative experience could be built on actual life circumstances and situations that have taken place over a period of time or these could be mythical in nature, which would mean that the person has heard some legends from different people or read the same somewhere and then tried to relate them with his own life (Bottum 2008). At times, it is also possible that these mythical experiences are true, which goes to suggest that people have faith in the authenticity of fables and stories that have been on the rounds since a long time. This indeed is a very fulfilling experience for the people who want to seek the best of both worlds, i.e. the real world as well as the one which is indeed based on myth and lacks sound logic. Moving on, the narrative abilities of a person help him to comprehend the real meaning of human life and this facilitates him in his quest to learn quite a number of things all this while. These different experiences therefore are very satisfying for him as he tries to relate the real experiences with the mythic ones, where the latter are just the stories that he has heard over a period of time and have little or no bearing on his life in essence (Author Unknown 2004). What is even more interesting is the fact that narrative and mythic experiences are based on the state of mind of a person, i.e. the exact situation under which he has listened to the fable which indeed is a part of the mythic experience or the real life story that he has been a part of, known as an actual narrative. In essence, in the time and age of today, it is of paramount significance if an individual tries his best to experience life’s different shades so that he could extract happiness, joy and delight from what is on offer in front of him. It would be a pity to suggest that those people, who do not like to narrate what is happening in front of them or with them, usually do not experience life completely and this means that they are missing out on a number of things in

OPM300 - Intro. to Operations Mgmt. CA Essay Example | Topics and Well Written Essays - 500 words - 1

OPM300 - Intro. to Operations Mgmt. CA - Essay Example uch, Event O is the most likely candidate because crashing Event O will result to a compensation of two weeks, which is just sufficient to compensate for the weeks lost in extending Event B. The key player in the success of a project is undoubtedly the project manager. Their roles include managing the project throughout the project cycle, balancing technical, schedule, and cost performance, solving problems expeditiously as they arise, and inspiring and motivating the entire team (Forsberg, Mooz, & Cotterman, 2005). With a project as extensive and large scale as the one described above, a project manager needs to have the right skills and tools in facing and overcoming the many different challenges that such a project can produce (Elearn Limited (Great Britain), 2005). One such challenge would be that project teams may be geographically dispersed. Since projects are usually collaborative efforts, different team members may be physically located in locations that may cause logistic challenges (Kerzner, 2009). Thus, a project manager must know how to ensure that logistic considerations are put in place when scheduling required events. Another problem that a project manager may face is the challenge of overbooked and mismanaged resources. Due to certain shortcoming, project teams may not have accurate information about their resources and what they are working on (Archibald, 2003). Some project teams may have more demand for projects than they have team members to execute the projects. Thus, a project manager has to be aware of these limitations and be alert enough to compensate