The Effects Of Technology On The Classroom - 1328 Words

On any given day, teens in the United States spend about nine hours using technology, according to a recent report (Common Sense Media). This nine hours is more time than teenagers spend sleeping, completing homework, or interacting with family. In recent years, constant access to the internet and social networking sites has created an addiction- a reliance that today’s youth can’t navigate around. Simultaneous with the greater presence of technology is greater success in the classroom. Over the past decade, the number of students who pass AP exams every year has quintupled (Forbes). But when it comes to basic skills such as holding a conversation, students are falling short. If the same amount of energy being put into teaching BC†¦show more content†¦The teachers who observed positive changes in students’ behavior gave credit to the education program, or some aspect of it, as contributing to the changes. Because of the pertinent lessons being taught in the classroom, the students were able and willing to apply what they learned at school to their daily lives. In the same way, if schools were to make communication curriculum a priority, there would be observable changes in the way students interact and carry themselves. This idea is shared by Sir Ken Robinson, New York Times bestselling author and emeritus professor of arts education at the University of Warwick. In his book Creative Schools, Robinson insists â€Å"The aims of education are to enable students to understand the world around them...so that they can become fulfilled individuals and active, compassionate citizens.† For most students, the classroom is the only place that kids will have the opportunity to learn the necessary skills that they need in order to thrive. With such a great role in teenagers’ lives, schools carry the responsibility of producing the successful citizens that Robinson is referring to. This being said, high school programs should in clude communication education given their significant impact on students’ behaviors and lives. The use of technology has diminished students’ communication skills. This is seen first hand by Paul Barnwell, high school teacher and writer for The Atlantic.Show MoreRelatedThe Effects Of Technology On The Classroom1519 Words   |  7 PagesClassrooms today look almost nothing like the classrooms of past generations. Modern classrooms revolve around technology, every room has either a projector or smart board front and center. A significant amount of homework is submitted digitally, and a computer is often a class requirement. Many studies have shown the positive effects these teaching and learning techniques have, and the results are not often disputed. Technology is helping educate students even at the elementary level, but studiesRead MoreTechnology And Its Effects On The Classroom960 Words   |  4 PagesIPads and all of this new technology is being used more to play Flappy Bird than get any real schoolwork done. With new innovations in technology, schools have decided to incorporate devices like the Smartboard or IPad, but we do not know how to use them effectively to teach. Sure, these devices could be of some help, but the school board has not effectively taught teachers how to fully utilize the very equipment they are using to teach their students. Even with a firewall that can block certainRead MoreThe Effect Of Technology On The Classroom Essay1210 Words   |  5 PagesThere is a growing trend in the use of technology in the classroom. As a teacher, I am always looking for ways to use manipulatives in my lessons to increase meaning and authenticity for students. I would love to keep my students engaged, motivated and interactive in the classroom and still be able to get through the content each day. In order to achieve this, I need to have an arsenal of tools to draw from. That is why I agree with (Tataroglu Erduran, 2010) as stated in the International ElectronicRead MoreThe Effects Of Technology On Our Classroom1166 Words   |  5 Pag esUsing technology in the classroom gives students a much greater advantage in whatever job or lifestyle they decide to pursue after their academic careers. Technology has become so much apart of our daily lives, and routines that we cannot expect the younger generation to be able to keep up unless they are equipped with the tools that are necessary. Some people might argue that inundating kids with too much technology can be harmful. Another argument against technology is that it is putting kids outRead MoreThe Effects Of Educational Technology On The Classroom1345 Words   |  6 PagesResearch Paper: Effects of Educational Technology In the Classroom By: Nicole Ault Computer Science 313 October 1st, 2017 Abstract: This research paper includes several studies on the effects of children’s learning when incorporating technology into their lives. Overall, the studies mentioned can make technology be viewed as an aid or a hinder on a child’s cognitive development. For some people the advances of technology in today’s world can be viewed asRead MoreTechnology And Its Effects On Our Classroom Essay1452 Words   |  6 Pages Technology In Classrooms When people walk into a classroom and a teacher is up front lecturing, all they see are heads down on desks. As they walk around people are sleeping and doodling things like â€Å"I love you†, and writing their names 1000 different ways. The room makes someone feel like they are standing in a funeral home. It is boring and no one pays any attention, and anyone could notice that when there is dried drool on the desks for the next class. Not all classrooms are bland thoughRead MoreThe Positive And Negative Effects Of Technology In The Classroom959 Words   |  4 Pagesthey’ve introduced technology into classrooms. More than anything, people question how much technology helps a student, as well as whether or not it actually hinders their learning. Both positive and negative effects have made themselves present, and both are continuing to grow in number. Whether liked or not, technology is a large part of today’s world, and people will only continue to use it as it grows. In classrooms today, both positives and negatives result from the use of technology, as well as fromRead MoreThe Effects Of Technology On Classroom Practices And Student Outcomes1564 Words   |  7 Pagesall participants should be ensured at all times and the research should be conducted in an ethical manner (National Health and Medical Research Council, 2015, p.5). In the aforementioned research, studying the effects the investment of technology throughout their school was having on classroom practices and student outcomes – specifically in mathematics and science teaching, many ethical considerations must be taken into account. Researchers must have received the appropriate consent from all stakeholdersRead MoreTechnology : Does Technology Help Or Hinder The Student?966 Words   |  4 Pagesviewpoints of today’s generation, and how technology has taken over and welcomed itself into many aspects of our lives. This course paper will take a look at one topi c of interest in particular, which in hopes will shed some light on a heavily discussed topic in the education world: does technology help or hinder the student. This paper will look to prove the point and discover more about the way in which technology has been incorporated into the classroom, both in an elementary context as well asRead MoreHow Personal Computers Affect Student s Learning Processes Essay1691 Words   |  7 Pagescentury, technology like personal computers and tablets have become more accessible and inexpensive. The aim of this research is to inform the public and education institutions on how personal computers affect student’s learning processes in the classroom. Most universities require the access to computers in order to perform task and write assignments. This has manifested in having more computers in a classroom used by the lecturers and students. The massive evolution and consumption of technology have

Feminism Types and Definitions Liberal - 1287 Words

Login Plans Pricing How It Works Courses Degrees Schools Careers | Register Search Courses Lessons Feminism Types and Definitions: Liberal, Socialist, Culture Radical / Sex and Gender in Society / Sociology 101: Intro to Sociology / Social Science / Courses Like? Feminism Types and Definitions: Liberal, Socialist, Culture Radical Video Quiz Congratulations! You ve reached the last video in the chapter. Transcript Start the Next Chapter Race and Ethnicity Definitions: Social Minority vs. Social Majority CREATE YOUR ACCOUNT Show Timeline Share This lesson first provides a general definition of feminism. Then, four specific types of feminism are discussed and defined, including liberal feminism, socialist†¦show more content†¦Radical feminists note that this traditional dichotomy maintains men as economically in power over women, and therefore, the traditional family structure should be rejected. Foreign Language History Humanities Socialist Feminism Math Science Radical feminism is the most extreme form. The second type of feminism, called socialist Social Science feminism, is slightly less extreme but still calls for major social change. Socialist feminism is a movement that calls for an end to capitalism High School Courses through a socialist reformation of our economy. Basically, socialist feminism argues that capitalism AP strengthens and supports the sexist status quo because men are the ones who currently have Socialist feminism calls for an end to capitalism Common Core power and money. Those men are more willing to share their power and money with other men, which GED means that women are continually given fewer opportunities and resources. This keeps women under the High School control of men. In short, socialist feminism focuses on economics and politics. They might point out the fact that in the United States women are typically paid only $0.70 for the exact same job that a man would be paid a Other Courses dollar for. Why are women paid less than men for the same work? Socialist feminists point out that this difference is based on a capitalist system.Show MoreRelatedDoes Feminism Create Equality?1037 Words   |  5 PagesDoes Feminism Create Equality? Feminism is an umbrella term for people who think there is something wrong with the idea that gender has the capability to limit an individual’s social and political right. Even if there is inequality between men and women, feminism has never been the main reason to give women their civil rights. Feminism started among European activists in the 19th century, when women were not treated equally and were not elected to high positions of power. Indeed, it sought to eliminateRead MoreFeminism : The Second Wave Of Feminism1222 Words   |  5 PagesWhat is feminism? Feminism is a definition to philosophy in which women and their contributions are valued. It is based on a social political and economical which is an equality for women. It’s a revolution that includes women and men who who wish the world to be equal without boundaries. The evolution of the rights of women in Australia owes much to successive waves of feminism, or the women s movement. The first of these took pl ace in the late 19th century and was concerned largely with gainingRead MorePolitical Feminism and its Misrepresentation1163 Words   |  5 Pagesthere is not just one kind of feminism, there are hundreds in each aspect of our life (Tavaana, 2014). The most under represented group within feminism is the kind that is in the government. Not all have the same theories, and therefore, do not have the same beliefs. However what we do know is that, whatever theory they have, or agenda they follow, they are all fierce promoters of gender equality. One theory of feminism that exists is the world is â€Å"Second Wave feminism† (Mandle, 2014). This is theRead MoreFeminism : A Social, Economic, And Social Equality Of The Sexes1465 Words   |  6 PagesFeminism. This seemingly harmless word can ruin or heighten a person’s reputation, it can give someone new views on the world, it can destroy relationships, it can build new ones; this single word can change lives. Most people categorize â€Å"feminism† as a code for women that tells them to hate men, not shave, burn bras, be vegan, and if there is any time left over maybe, just maybe, to fight for women’s rights. Now, there are definitely feminists that fulfill this stereotype but the vast majority ofRead MoreFeminism : The First Wave Of Feminism1267 Words   |  6 PagesFeminism is a movement calling for social change, holding to a belief that women are oppressed by American society due to patriarchy’s inherent sexism. This social movement explained quite simply started in the 19th century when women fought for the right to vote, sought to improve workplace conditions for women as well as increase working opportunities. From this initial movement, called first wa ve feminism, stemmed other waves that though somewhere in the same vein, they held many differing goalsRead MoreFeminism Theory : Who Want Women Equality, They Should Look Into Feminism1552 Words   |  7 PagesShelby Milinovich Mrs. Almack English 4 AP September 21, 2014 Feminism Theory To those who want women equality, they should look into feminism. To be a feminist you don’t have to be a woman, you just need to support women in their fight to be legally equal to men in social and economical situations. This means women deserve equal pay, equal access to education, make decisions about their own body, ending job sex segregation, better working conditions, for women to be able to hold a public officeRead MoreFeminism And The World Of The 2016 Election884 Words   |  4 PagesFeminism and Intersectionality are at the forefront of the 2016 election. While feminism is still viewed in somewhat limited terms of promoting the equality and status of women, Intersectionality is defined in much broader language, as the interconnection of race, gender, ability, and class in the social world. Moreover, all of these intersecting categories overlap and cannot be separated. Thus, the traditional view of feminism, that promotes the equality of women first and foremost, is often pittedRead MoreWomen During The 19th Century Essay1107 Words   |  5 Pagesdecades after, pants would be allowed, introducing scandalous shorts along with it. Although heavily criticized, the stigma of shorts lessened, showing us more familiar styles. Dress reform went full force in this era. Fashion during the second-wave feminism was marked the increase of more comfortable clothing. Women were primarily working in factories for the war effort, so their dress was consisted mainly of pants and high collared shirts []. Fashion in this era would eventually go towards flashy paddedRead MoreFeminism1121 Words   |  5 PagesFEMINISM Introduction to Sociology Feminism Belief in the social, political, and economic equality of the sexes. The movement organized around this belief. Feminism Feminist Theory is an outgrowth of the general movement to empower women worldwide. Feminism can be defined as a recognition and critique of male supremacy combined with efforts to change it. Feminism The goals of feminism are: To demonstrate the importance of women To reveal that historically women have been subordinate to menRead MoreFeminism And Gender And Ethnic Studies1172 Words   |  5 PagesMy Interpretation of Feminism Feminism has had a deep impact on me since I was infantile. Though she never mentioned it, my mother was an active feminist. I grew up playing with toys considered to belong to either sex. I was taught to be strong and to let my emotions out, and I was given freedom to make my own identity. This was my first experience of social feminism, followed years later by learning the definition of feminism and learning to also see discrimination politically and economically

Bayesian Inference Free Essays

string(34) " in the context of a binary GLMM\." Biostatistics (2010), 11, 3, pp. 397–412 doi:10. 1093/biostatistics/kxp053 Advance Access publication on December 4, 2009 Bayesian inference for generalized linear mixed models YOUYI FONG Downloaded from http://biostatistics. We will write a custom essay sample on Bayesian Inference or any similar topic only for you Order Now oxfordjournals. org/ at Cornell University Library on April 20, 2013 Department of Biostatistics, University of Washington, Seattle, WA 98112, USA ? HAVARD RUE Department of Mathematical Sciences, The Norwegian University for Science and Technology, N-7491 Trondheim, Norway JON WAKEFIELD? Departments of Statistics and Biostatistics, University of Washington, Seattle, WA 98112, USA jonno@u. ashington. edu S UMMARY Generalized linear mixed models (GLMMs) continue to grow in popularity due to their ability to directly acknowledge multiple levels of dependency and model different data types. For small sample sizes especially, likelihood-based inference can be unreliable with variance components being particularly difficult to estimate. A Bayesian approach is appealing but has been hampered by the lack of a fast implementation, and the difficulty in specifying prior distributions with variance components again being particularly problematic. Here, we briefly review previous approaches to computation in Bayesian implementations of GLMMs and illustrate in detail, the use of integrated nested Laplace approximations in this context. We consider a number of examples, carefully specifying prior distributions on meaningful quantities in each case. The examples cover a wide range of data types including those requiring smoothing over time and a relatively complicated spline model for which we examine our prior specification in terms of the implied degrees of freedom. We conclude that Bayesian inference is now practically feasible for GLMMs and provides an attractive alternative to likelihood-based approaches such as penalized quasi-likelihood. As with likelihood-based approaches, great care is required in the analysis of clustered binary data since approximation strategies may be less accurate for such data. Keywords: Integrated nested Laplace approximations; Longitudinal data; Penalized quasi-likelihood; Prior specification; Spline models. 1. I NTRODUCTION Generalized linear mixed models (GLMMs) combine a generalized linear model with normal random effects on the linear predictor scale, to give a rich family of models that have been used in a wide variety of applications (see, e. g. Diggle and others, 2002; Verbeke and Molenberghs, 2000, 2005; McCulloch and others, 2008). This flexibility comes at a price, however, in terms of analytical tractability, which has a ? To whom correspondence should be addressed. c The Author 2009. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals. permissions@oxfordjournals. rg. 398 Y. F ONG AND OTHERS number of implications including computational complexity, and an unknown degree to which inference is dependent on modeling assumptions. Likelihood-based inference may be carried out relatively easily within many software platforms (except perhaps for binary responses), but inference is dependent on asymptotic sampling distributions of estimato rs, with few guidelines available as to when such theory will produce accurate inference. A Bayesian approach is attractive, but requires the specification of prior distributions which is not straightforward, in particular for variance components. Computation is also an issue since the usual implementation is via Markov chain Monte Carlo (MCMC), which carries a large computational overhead. The seminal article of Breslow and Clayton (1993) helped to popularize GLMMs and placed an emphasis on likelihood-based inference via penalized quasi-likelihood (PQL). It is the aim of this article to describe, through a series of examples (including all of those considered in Breslow and Clayton, 1993), how Bayesian inference may be performed with computation via a fast implementation and with guidance on prior specification. The structure of this article is as follows. In Section 2, we define notation for the GLMM, and in Section 3, we describe the integrated nested Laplace approximation (INLA) that has recently been proposed as a computationally convenient alternative to MCMC. Section 4 gives a number of prescriptions for prior specification. Three examples are considered in Section 5 (with additional examples being reported in the supplementary material available at Biostatistics online, along with a simulation study that reports the performance of INLA in the binary response situation). We conclude the paper with a discussion in Section 6. 2. T HE G ENERALIZED LINEAR MIXED MODEL GLMMs extend the generalized linear model, as proposed by Nelder and Wedderburn (1972) and comprehensively described in McCullagh and Nelder (1989), by adding normally distributed random effects on the linear predictor scale. Suppose Yi j is of exponential family form: Yi j |? i j , ? 1 ? p(†¢), where p(†¢) is a member of the exponential family, that is, p(yi j |? i j , ? 1 ) = exp yi j ? i j ? b(? i j ) + c(yi j , ? 1 ) , a(? 1 ) Downloaded from http://biostatistics. oxfordjournals. org/ at Cornell University Library on April 20, 2013 for i = 1, . . . , m units (clusters) and j = 1, . . , n i , measurements per unit and where ? i j is the (scalar) ? canonical parameter. Let ? i j = E[Yi j |? , b i , ? 1 ] = b (? i j ) with g(? i j ) = ? i j = x i j ? + z i j b i , where g(†¢) is a monotonic â€Å"link† function, x i j is 1 ? p, and z i j is 1 ? q, with ? a p ? 1 vector of fixed ? Q effects and b i a q ? 1 vector of random ef fects, hence ? i j = ? i j (? , b i ). Assume b i |Q ? N (0, Q ? 1 ), where ? the precision matrix Q = Q (? 2 ) depends on parameters ? 2 . For some choices of model, the matrix Q is singular; examples include random walk models (as considered in Section 5. ) and intrinsic conditional ? autoregressive models. We further assume that ? is assigned a normal prior distribution. Let ? = (? , b ) denote the G ? 1 vector of parameters assigned Gaussian priors. We also require priors for ? 1 (if not a constant) and for ? 2 . Let ? = (? 1 , ? 2 ) be the variance components for which non-Gaussian priors are ? assigned, with V = dim(? ). 3. I NTEGRATED NESTED L APLACE APPROXIMATION Before the MCMC revolution, there were few examples of the applications of Bayesian GLMMs since, outside of the linear mixed model, the models are analytically intractable. Kass and Steffey (1989) describe the use of Laplace approximations in Bayesian hierarchical models, while Skene and Wakefield Bayesian GLMMs 399 (1990) used numerical integration in the context of a binary GLMM. You read "Bayesian Inference" in category "Papers" The use of MCMC for GLMMs is particularly appealing since the conditional independencies of the model may be exploited when the required conditional distributions are calculated. Zeger and Karim (1991) described approximate Gibbs sampling for GLMMs, with nonstandard conditional distributions being approximated by normal distributions. More general Metropolis–Hastings algorithms are straightforward to construct (see, e. g. Clayton, 1996; Gamerman, 1997). The winBUGS (Spiegelhalter, Thomas, and Best, 1998) software example manuals contain many GLMM examples. There are now a variety of additional software platforms for fitting GLMMs via MCMC including JAGS (Plummer, 2009) and BayesX (Fahrmeir and others, 2004). A large practical impediment to data analysis using MCMC is the large computational burden. For this reason, we now briefly review the INLA computational approach upon which we concentrate. The method combines Laplace approximations and numerical integration in a very efficient manner (see Rue and others, 2009, for a more extensive treatment). For the GLMM described in Section 2, the posterior is given by m Downloaded from http://biostatistics. oxfordjournals. org/ at Cornell University Library on April 20, 2013 ? y ? ? ? ?(? , ? |y ) ? ?(? |? )? (? ) i=1 y ? p(y i |? , ? ) m i=1 1 ? ? Q ? ? b ? ?(? )? (? )|Q (? 2 )|1/2 exp ? b T Q (? 2 )b + 2 y ? log p(y i |? , ? 1 ) , where y i = (yi1 , . . . , yin i ) is the vector of observations on unit/cluster i. We wish to obtain the posterior y y marginals ? (? g |y ), g = 1, . . . , G, and ? (? v |y ), v = 1, . . . , V . The number of variance components, V , should not be too large for accurate inference (since these components are integrated out via Cartesian product numerical integration, which does not scale well with dimension). We write y ? (? g |y ) = which may be evaluated via the approximation y ? (? g |y ) = K ? ? y ? ?(? g |? , y ) ? ?(? |y )d? , ? ? y ? ?(? g |? , y ) ? ? (? |y )d? ? y ? ? (? g |? k , y ) ? ? (? k |y ) ? k, ? (3. 1) k=1 here Laplace (or other related analytical approximations) are applied to carry out the integrations required ? ? for evaluation of ? (? g |? , y ). To produce the grid of points {? k , k = 1, . . . , K } over which numerical inte? y gration is performed, the mode of ? (? |y ) is located, and the Hessian is approximated, from which the grid is created and exploited in (3. 1). The output of INLA consists of posterior marginal distributions, which can be summarized via means, variances, and quantiles. Importantly for model comparison, the normaly izing constant p(y ) is calculated. The evaluation of this quantity is not straightforward using MCMC (DiCiccio and others, 1997; Meng and Wong, 1996). The deviance information criterion (Spiegelhalter, Best, and others, 1998) is popular as a model selection tool, but in random-effects models, the implicit approximation in its use is valid only when the effective number of parameters is much smaller than the number of independent observations (see Plummer, 2008). 400 Y. F ONG AND OTHERS 4. P RIOR DISTRIBUTIONS 4. 1 Fixed effects Recall that we assume ? is normally distributed. Often there will be sufficient information in the data for ? o be well estimated with a normal prior with a large variance (of course there will be circumstances under which we would like to specify more informative priors, e. g. when there are many correlated covariates). The use of an improper prior for ? will often lead to a proper posterior though care should be taken. For example, Wakefield (2007) shows that a Poisson likelihood with a linea r link can lead to an improper posterior if an improper prior is used. Hobert and Casella (1996) discuss the use of improper priors in linear mixed effects models. If we wish to use informative priors, we may specify independent normal priors with the parameters for each component being obtained via specification of 2 quantiles with associated probabilities. For logistic and log-linear models, these quantiles may be given on the exponentiated scale since these are more interpretable (as the odds ratio and rate ratio, respectively). If ? 1 and ? 2 are the quantiles on the exponentiated scale and p1 and p2 are the associated probabilities, then the parameters of the normal prior are given by ? = ? = z 2 log(? 1 ) ? z 1 log(? 2 ) , z2 ? 1 Downloaded from http://biostatistics. oxfordjournals. org/ at Cornell University Library on April 20, 2013 log(? 2 ) ? log(? 1 ) , z2 ? z1 where z 1 and z 2 are the p1 and p2 quantiles of a standard normal random variable. For example, in an epidemiological context, we may wish to specify a prior on a relative risk parameter, exp(? 1 ), which has a median of 1 and a 95% point of 3 (if we think it is unlikely that the relative risk associated with a unit increase in exposure exceeds 3). These specifications lead to ? 1 ? N (0, 0. 6682 ). 4. 2 Variance components We begin by describing an approach for choosing a prior for a single random effect, based on Wakefield (2009). The basic idea is to specify a range for the more interpretable marginal distribution of bi and use this to drive specification of prior parameters. We state a trivial lemma upon which prior specification is based, but first define some notation. We write ? ? Ga(a1 , a2 ) for the gamma distribution with un? normalized density ? a1 ? 1 exp(? a2 ? ). For q-dimensional x , we write x ? Tq (? , , d) for the Student’s x x t distribution with unnormalized density [1 + (x ? ? )T ? 1 (x ? )/d]? (d+q)/2 . This distribution has location ? , scale matrix , and degrees of freedom d. L EMMA 1 Let b|? ? N (0, ? ?1 ) and ? ? Ga(a1 , a2 ). Integration over ? gives the marginal distribution of b as T1 (0, a2 /a1 , 2a1 ). To decide upon a prior, we give a range for a generic random effect b and specify the degrees of freev d dom, d, and then solve for a1 and a2 . For the range (? R, R) , we use the relationship  ±t1? (1? q)/2 a2 /a1 = d  ±R, where tq is the 100 ? qth quantile of a Student t random variable with d degrees of freedom, to give d a1 = d/2 and a2 = R 2 d/2(t1? (1? q)/2 )2 . In the linear mixed effects model, b is directly interpretable, while for binomial or Poisson models, it is more appropriate to think in terms of the marginal distribution of exp(b), the residual odds and rate ratio, respectively, and this distribution is log Student’s t. For example, if we choose d = 1 (to give a Cauchy marginal) and a 95% range of [0. 1, 10], we take R = log 10 and obtain a = 0. 5 and b = 0. 0164. Bayesian GLMMs 401 ?1 Another convenient choice is d = 2 to give the exponential distribution with mean a2 for ? ?2 . This leads to closed-form expressions for the more interpretable quantiles of ? o that, for example, if we 2 specify the median for ? as ? m , we obtain a2 = ? m log 2. Unfortunately, the use of Ga( , ) priors has become popular as a prior for ? ?2 in a GLMM context, arising from their use in the winBUGS examples manual. As has been pointed out many times (e. g. Kelsall and Wakefield, 1999; Gelman, 2006; Crainiceanu and others, 2008), this choice pl aces the majority of the prior mass away from zero and leads to a marginal prior for the random effects which is Student’s t with 2 degrees of freedom (so that the tails are much heavier than even a Cauchy) and difficult to justify in any practical setting. We now specify another trivial lemma, but first establish notation for the Wishart distribution. For the q ? q nonsingular matrix z , we write z ? Wishartq (r, S ) for the Wishart distribution with unnormalized Downloaded from http://biostatistics. oxfordjournals. org/ at Cornell University Library on April 20, 2013 Q Lemma: Let b = (b1 , . . . , bq ), with b |Q ? iid Nq (0, Q ? 1 ), Q ? Wishartq (r, S ). Integration over Q b as Tq (0, [(r ? q + 1)S ]? 1 , r ? q + 1). S gives the marginal distribution of The margins of a multivariate Student’s t are t also, which allows r and S to be chosen as in the univariate case. Specifically, the kth element of a generic random effect, bk , follows a univariate Student t distribution with location 0, scale S kk /(r ? q + 1), and degrees of freedom d = r ? q + 1, where S kk d is element (k, k) of the inverse of S . We obtain r = d + q ? 1 and S kk = (t1? (1? q)/2 )2 /(d R 2 ). If a priori b are correlated we may specify S jk = 0 for j = k and we have no reason to believe that elements of S kk = 1/Skk , to recover the univariate specification, recognizing that with q = 1, the univariate Wishart has parameters a1 = r/2 and a2 = 1/(2S). If we believe that elements of b are dependent then we may specify the correlations and solve for the off-diagonal elements of S . To ensure propriety of the posterior, proper priors are required for ; Zeger and Karim (1991) use an improper prior for , so that the posterior is improper also. 4. 3 Effective degrees of freedom variance components prior z z z z density |z |(r ? q? 1)/2 exp ? 1 tr(z S ? 1 ) . This distribution has E[z ] = r S and E[z ? 1 ] = S ? 1 /(r ? q ? 1), 2 and we require r q ? 1 for a proper distribution. In Section 5. 3, we describe the GLMM representation of a spline model. A generic linear spline model is given by K yi = x i ? + k=1 z ik bk + i , where x i is a p ? 1 vector of covariates with p ? 1 associated fixed effects ? , z ik denote the spline 2 basis, bk ? iid N (0, ? b ), and i ? iid N (0, ? 2 ), with bk and i independent. Specification of a prior for 2 is not straightforward, but may be of great importance since it contributes to determining the amount ? b of smoothing that is applied. Ruppert and others (2003, p. 77) raise concerns, â€Å"about the instability of automatic smoothing parameter selection even for single predictor models†, and continue, â€Å"Although we are attracted by the automatic nature of the mixed model-REML approach to fitting additive models, we discourage blind acceptance of whatever answer it provides and recommend looking at other amounts of smoothing†. While we would echo this general advice, we believe that a Bayesian mixed model approach, with carefully chosen priors, can increase the stability of the mixed model representation. There has been 2 some discussion of choice of prior for ? in a spline context (Crainiceanu and others, 2005, 2008). More general discussion can be found in Natarajan and Kass (2000) and Gelman (2006). In practice (e. g. Hastie and Tibshirani, 1990), smoothers are often applied with a fixed degrees of freedom. We extend this rationale by examining the prior degrees of freedom that is implied by the choice 402 Y. F ONG AND OTHERS ?2 ? b ? Ga(a1 , a2 ). For the general linear mixed model y = x ? + zb + , we have x z where C = [x |z ] is n ? ( p + K ) and C y = x ? + z b = C (C T C + 0 p? p 0K ? p )? 1 C T y , = 0 p? K 2 cov(b )? 1 b ? )? 1 C T C }, Downloaded from http://biostatistics. xfordjournals. org/ at Cornell University Library on April 20, 2013 (see, e. g. Ruppert and others, 2003, Section 8. 3). The total degrees of freedom associated with the model is C df = tr{(C T C + which may be decomposed into the degrees of freedom associated with ? and b , and extends easily to situations in which we have additional random effects, beyond those associated with the spline basis (such an example is considered in Section 5. 3). In each of these situations, the degrees of freedom associated C with the respective parameter is obtained by summing the appropriate diagonal elements of (C T C + )? C T C . Specifically, if we have j = 1, . . . , d sets of random-effect parameters (there are d = 2 in the model considered in Section 5. 3) then let E j be the ( p + K ) ? ( p + K ) diagonal matrix with ones in the diagonal positions corresponding to set j. Then the degrees of freedom associated with this set is E C df j = tr{E j (C T C + )? 1 C T C . Note that the effective degrees of freedom changes as a function of K , as expected. To evaluate , ? 2 is required. If we specify a proper prior for ? 2 , then we may specify the 2 2 joint prior as ? (? b , ? 2 ) = ? (? 2 )? (? b |? 2 ). Often, however, we assume the improper prior ? (? 2 ) ? 1/? 2 since the data provide sufficient information with respect to ? 2 . Hence, we have found the substitution of an estimate for ? 2 (for example, from the fitting of a spline model in a likelihood implementation) to be a practically reasonable strategy. As a simple nonspline demonstration of the derived effective degrees of freedom, consider a 1-way analysis of variance model Yi j = ? 0 + bi + i j 2 with bi ? iid N (0, ? b ), i j ? iid N (0, ? 2 ) for i = 1, . . . , m = 10 groups and j = 1, . . . , n = 5 observa? 2 tions per group. For illustration, we assume ? ? Ga(0. 5, 0. 005). Figure 1 displays the prior distribution for ? , the implied prior distribution on the effective degrees of freedom, and the bivariate plot of these quantities. For clarity of plotting, we exclude a small number of points beyond ? 2. 5 (4% of points). In panel (c), we have placed dashed horizontal lines at effective degrees of freedom equal to 1 (c omplete smoothing) and 10 (no smoothing). From panel (b), we conclude that here the prior choice favors quite strong smoothing. This may be contrasted with the gamma prior with parameters (0. 001, 0. 001), which, in this example, gives reater than 99% of the prior mass on an effective degrees of freedom greater than 9. 9, again showing the inappropriateness of this prior. It is appealing to extend the above argument to nonlinear models but unfortunately this is not straightforward. For a nonlinear model, the degrees of freedom may be approximated by C df = tr{(C T W C + where W = diag Vi? 1 d? i dh 2 )? 1 C T W C }, and h = g ? 1 denotes the inverse link function. Unfortunately, this quantity depends on ? and b , which means that in practice, we would have to use prior estimates for all of the parameters, which may not be practically possible. Fitting the model using likelihood and then substituting in estimates for ? and b seems philosophically dubious. Bayesian GLMMs 403 Downloaded from http://biostatistics. oxfordjournals. org/ at Cornell University Library on April 20, 2013 Fig. 1. Gamma prior for ? ?2 with parameters 0. 5 and 0. 005, (a) implied prior for ? , (b) implied prior for the effective degrees of freedom, and (c) effective degrees of freedom versus ? . 4. 4 Random walk models Conditionally represented smoothing models are popular for random effects in both temporal and spatial applications (see, e. g. Besag and others, 1995; Rue and Held, 2005). For illustration, consider models of the form ? (m? r ) Q u 2 exp ? p(u |? u ) = (2? )? (m? r )/2 |Q |1/2 ? u 1 T u Qu , 2 2? u (4. 1) 404 Y. F ONG AND OTHERS where u = (u 1 , . . . , u m ) is the collection of random effects, Q is a (scaled) â€Å"precision† matrix of rank Q m ? r , whose form is determined by the application at hand, and |Q | is a generalized determinant which is the product over the m ? r nonzero eigenvalues of Q . Picking a prior for ? u is not straightforward because ? u has an interpretation as the conditional standard deviation, where the elements that are conditioned upon depends on the application. We may simulate realizations from (4. 1) to examine candidate prior distributions. Due to the rank deficiency, (4. 1) does not define a probability density, and so we cannot directly simulate from this prior. However, Rue and Held (2005) give an algorithm for generating samples from (4. 1): 1. Simulate z j ? N (0, 1 ), for j = m ? r + 1, . . . , m, where ? j are the eigenvalues of Q (there are j m ? r nonzero eigenvalues as Q has rank m ? r ). 2. Return u = z m? r +1 e n? r +1 + z 3 e 3 + †¢ †¢ †¢ + z n e m = E z , where e j are the corresponding eigenvectors of Q , E is the m ? (m ? ) matrix with these eigenvectors as columns, and z is the (m ? r ) ? 1 vector containing z j , j = m ? r + 1, . . . , m. The simulation algorithm is conditioned so that samples are zero in the null-space of Q ; if u is a sample and the null-space is spanned by v 1 and v 2 , then u T v 1 = u T v 2 = 0. For example, suppose Q 1 = 0 so that the null-space is spanned by 1, and the rank defici ency is 1. Then Q is improper since the eigenvalue corresponding to 1 is zero, and samples u produced by the algorithm are such that u T 1 = 0. In Section 5. 2, we use this algorithm to evaluate different priors via simulation. It is also useful to note that if we wish to compute the marginal variances only, simulation is not required, as they are available as the diagonal elements of the matrix j 1 e j e T . j j 5. E XAMPLES Here, we report 3 examples, with 4 others described in the supplementary material available at Biostatistics online. Together these cover all the examples in Breslow and Clayton (1993), along with an additional spline example. In the first example, results using the INLA numerical/analytical approximation described in Section 3 were compared with MCMC as implemented in the JAGS software (Plummer, 2009) and found to be accurate. For the models considered in the second and third examples, the approximation was compared with the MCMC implementation contained in the INLA software. 5. 1 Longitudinal data We consider the much analyzed epilepsy data set of Thall and Vail (1990). These data concern the number ? of seizures, Yi j for patient i on visit j, with Yi j |? , b i ? ind Poisson(? i j ), i = 1, . . . , 59, j = 1, . . . , 4. We concentrate on the 3 random-effects models fitted by Breslow and Clayton (1993): log ? i j = x i j ? + b1i , (5. 1) (5. 2) (5. 3) Downloaded from http://biostatistics. oxfordjournals. rg/ at Cornell University Library on April 20, 2013 log ? i j = x i j ? + b1i + b2i V j /10, log ? i j = x i j ? + b1i + b0i j , where x i j is a 1 ? 6 vector containing a 1 (representing the intercept), an indicator for baseline measurement, a treatment indicator, the baseline by treatment interaction, which is the parameter of interest, age, and either an indicator of the fourth visit (models (5. 1) an d (5. 2) and denoted V4 ) or visit number coded ? 3, ? 1, +1, +3 (model (5. 3) and denoted V j /10) and ? is the associated fixed effect. All 3 models 2 include patient-specific random effects b1i ? N 0, ? , while in model (5. 2), we introduce independent 2 ). Model (5. 3) includes random effects on the slope associated with â€Å"measurement errors,† b0i j ? N (0, ? 0 Bayesian GLMMs 405 Table 1. PQL and INLA summaries for the epilepsy data Variable Base Trt Base ? Trt Age V4 or V/10 ? 0 ? 1 ? 2 Model (5. 1) PQL 0. 87  ± 0. 14 ? 0. 91  ± 0. 41 0. 33  ± 0. 21 0. 47  ± 0. 36 ? 0. 16  ± 0. 05 — 0. 53  ± 0. 06 — INLA 0. 88  ± 0. 15 ? 0. 94  ± 0. 44 0. 34  ± 0. 22 0. 47  ± 0. 38 ? 0. 16  ± 0. 05 — 0. 56  ± 0. 08 — Model (5. 2) PQL 0. 86  ± 0. 13 ? 0. 93  ± 0. 40 0. 34  ± 0. 21 0. 47  ± 0. 35 ? 0. 10  ± 0. 09 0. 36  ± 0. 04 0. 48  ± 0. 06 — INLA 0. 8  ± 0. 15 ? 0. 96  ± 0. 44 0. 35  ± 0. 23 0. 48  ± 0. 39 ? 0. 10  ± 0. 09 0. 41  ± 0. 04 0. 53  ± 0. 07 — Model (5. 3) PQL 0. 87  ± 0. 14 ? 0. 91  ± 0. 41 0. 33  ± 0. 21 0. 46  ± 0. 36 ? 0. 26  ± 0. 16 — 0. 52  ± 0. 06 0. 74  ± 0. 16 INLA 0. 88  ± 0. 14 ? 0. 94  ± 0. 44 0. 34  ± 0. 22 0. 47  ± 0. 38 ? 0. 27  ± 0. 16 — 0. 56  ± 0. 06 0. 70  ± 0. 14 Downloaded from http://biostatistics. oxfordjournals. org/ at Cornell University Library on April 20, 2013 visit, b2i with b1i b2i ? N (0, Q ? 1 ). (5. 4) We assume Q ? Wishart(r, S ) with S = S11 S12 . For prior specification, we begin with the bivariate S21 S22 model and assume that S is diagonal. We assume the upper 95% point of the priors for exp(b1i ) and exp(b2i ) are 5 and 4, respectively, and that the marginal distributions are t with 4 degrees of freedom. Following the procedure outlined in Section 4. 2, we obtain r = 5 and S = diag(0. 439, 0. 591). We take ? 2 the prior for ? 1 in model (5. 1) to be Ga(a1 , a2 ) with a1 = (r ? 1)/2 = 2 and a2 = 1/2S11 = 1. 140 (so that this prior coincides with the marginal prior obtained from the bivariate specification). In model (5. 2), ? 2 ? 2 we assume b1i and b0i j are independent, and that ? 0 follows the same prior as ? , that is, Ga(2, 1. 140). We assume a flat prior on the intercept, and assume that the rate ratios, exp(? j ), j = 1, . . . , 5, lie between 0. 1 and 10 with probability 0. 95 which gives, using the approach described in Section 4. 1, a normal prior with mean 0 and variance 1. 172 . Table 1 gives PQL and INLA summaries for models (5. 1–5. 3). There are some differences between the PQL and Bayesian analyse s, with slightly larger standard deviations under the latter, which probably reflects that with m = 59 clusters, a little accuracy is lost when using asymptotic inference. There are some differences in the point estimates which is at least partly due to the nonflat priors used—the priors have relatively large variances, but here the data are not so abundant so there is sensitivity to the prior. Reassuringly under all 3 models inference for the baseline-treatment interaction of interest is virtually y identical and suggests no significant treatment effect. We may compare models using log p(y ): for 3 models, we obtain values of ? 674. 8, ? 638. 9, and ? 665. 5, so that the second model is strongly preferred. 5. Smoothing of birth cohort effects in an age-cohort model We analyze data from Breslow and Day (1975) on breast cancer rates in Iceland. Let Y jk be the number of breast cancer of cases in age group j (20–24,. . . , 80–84) and birth cohort k (1840–1849,. . . ,1940–1949) with j = 1, . . . , J = 13 and k = 1, . . . , K = 11. Following Breslow and Clayton (1993), we assume Y jk |? jk ? ind Poisson(? jk ) with log ? jk = log n jk + ? j + ? k + vk + u k (5. 5) and where n jk is the person-years denominator, exp(? j ), j = 1, . . . , J , represent fixed effects for age relative risks, exp(? is the relative risk associated with a one group increase in cohort group, vk ? iid 406 Y. F ONG AND OTHERS 2 N (0, ? v ) represent unstructured random effects associated with cohort k, with smooth cohort terms u k following a second-order random-effects model with E[u k |{u i : i k}] = 2u k? 1 ? u k? 2 and Var(u k |{u i : 2 i k}) = ? u . This latter model is to allow the rates to vary smoothly with cohort. An equivalent representation of this model is, for 2 k K ? 1, 1 E[u k |{u l : l = k}] = (4u k? 1 + 4u k+1 ? u k? 2 ? u k+2 ), 6 Var(u k |{u l : l = k}) = 2 ? . 6 Downloaded from http://biostatistics. oxfordjournals. org/ at Cornell University Library on April 20, 2013 The rank of Q in the (4. 1) representation of this model is K ? 2 reflecting that both the overall level and the overall trend are aliase d (hence the appearance of ? in (5. 5)). The term exp(vk ) reflects the unstructured residual relative risk and, following the argument in Section 4. 2, we specify that this quantity should lie in [0. 5, 2. 0] with probability 0. 95, with a marginal log Cauchy ? 2 distribution, to obtain the gamma prior ? v ? Ga(0. 5, 0. 00149). The term exp(u k ) reflects the smooth component of the residual relative risk, and the specification of a 2 prior for the associated variance component ? u is more difficult, given its conditional interpretation. Using the algorithm described in Section 4. 2, we examined simulations of u for different choices of gamma ? 2 hyperparameters and decided on the choice ? u ? Ga(0. 5, 0. 001); Figure 2 shows 10 realizations from the prior. The rationale here is to examine realizations to see if they conform to our prior expectations and in particular exhibit the required amount of smoothing. All but one of the realizations vary smoothly across the 11 cohorts, as is desirable. Due to the tail of the gamma distribution, we will always have some extreme realizations. The INLA results, summarized in graphical form, are presented in Figure 2(b), alongside likelihood fits in which the birth cohort effect is incorporated as a linear term and as a factor. We see that the smoothing model provides a smooth fit in birth cohort, as we would hope. 5. 3 B-Spline nonparametric regression We demonstrate the use of INLA for nonparametric smoothing using O’Sullivan splines, which are based on a B-spline basis. We illustrate using data from Bachrach and others (1999) that concerns longitudinal measurements of spinal bone mineral density (SBMD) on 230 female subjects aged between 8 and 27, and of 1 of 4 ethnic groups: Asian, Black, Hispanic, and White. Let yi j denote the SBMD measure for subject i at occasion j, for i = 1, . . . , 230 and j = 1, . . . , n i with n i being between 1 and 4. Figure 3 shows these data, with the gray lines indicating measurements on the same woman. We assume the model K Yi j = x i ? 1 + agei j ? 2 + k=1 z i jk b1k + b2i + ij, where x i is a 1 ? vector containing an indicator for the ethnicity of individual i, with ? 1 the associated 4 ? 1 vector of fixed effects, z i jk is the kth basis associated with age, with associated parameter b1k ? 2 2 N (0, ? 1 ), and b2i ? N (0, ? 2 ) are woman-specific random effects, finally, i j ? iid N (0, ? 2 ). All random terms are assumed independent. Note that the spline model is assumed common to all ethnic groups and all women , though it would be straightforward to allow a different spline for each ethnicity. Writing this model in the form y = x ? + z 1b1 + z 2b 2 + = C ? + . Bayesian GLMMs 407 Downloaded from http://biostatistics. oxfordjournals. org/ at Cornell University Library on April 20, 2013 Fig. 2. (a) Ten realizations (on the relative risk scale) from the random effects second-order random walk model in which the prior on the random-effects precision is Ga(0. 5,0. 001), (b) summaries of fitted models: the solid line corresponds to a log-linear model in birth cohort, the circles to birth cohort as a factor, and â€Å"+† to the Bayesian smoothing model. we use the method described in Section 4. 3 to examine the effective number of parameters implied by the ? 2 ? 2 priors ? 1 ? Ga(a1 , a2 ) and ? 2 ? Ga(a3 , a4 ). To fit the model, we first use the R code provided in Wand and Ormerod (2008) to construct the basis functions, which are then input to the INLA program. Running the REML version of the model, we obtain 2 ? = 0. 033 which we use to evaluate the effective degrees of freedoms associated with priors for ? 1 and 2 . We assume the usual improper prior, ? (? 2 ) ? 1/? 2 for ? 2 . After some experimentation, we settled ? 2 408 Y. F ONG AND OTHERS Downloaded from http://biostatistics. oxfordjournals. org/ at Cornell University Library on April 20, 2013 Fig. 3. SBMD versus age by ethnicity. Measurements on the same woman are joined with gray lines. The solid curve corresponds to the fitted spline and the dashed lines to the individual fits. ?2 2 on the prior ? 1 ? Ga(0. 5, 5 ? 10? 6 ). For ? 2 , we wished to have a 90% interval for b2i of  ±0. 3 which, ? 2 with 1 degree of freedom for the marginal distribution, leads to ? 2 ? Ga(0. 5, 0. 00113). Figure 4 shows the priors for ? 1 and ? 2 , along with the implied effective degrees of freedom under the assumed priors. For the spline component, the 90% prior interval for the effective degrees of freedom is [2. 4,10]. Table 2 compares estimates from REML and INLA implementations of the model, and we see close correspondence between the 2. Figure 4 also shows the posterior medians for ? 1 and ? 2 and for the 2 effective degrees of freedom. For the spline and random effects these correspond to 8 and 214, respectively. The latter figure shows that there is considerable variability between the 230 women here. This is confirmed in Figure 3 where we observe large vertical differences between the profiles. This figure also shows the fitted spline, which appears to mimic the trend in the data well. 5. 4 Timings For the 3 models in the longitudinal data example, INLA takes 1 to 2 s to run, using a single CPU. To get estimates with similar precision with MCMC, we ran JAGS for 100 000 iterations, which took 4 to 6 min. For the model in the temporal smoothing example, INLA takes 45 s to run, using 1 CPU. Part of the INLA procedure can be executed in a parallel manner. If there are 2 CPUs available, as is the case with today’s prevalent INTEL Core 2 Duo processors, INLA only takes 27 s to run. It is not currently possible to implement this model in JAGS. We ran the MCMC utility built into the INLA software for 3. 6 million iterations, to obtain estimates of comparable accuracy, which took 15 h. For the model in the B-spline nonparametric regression example, INLA took 5 s to run, using a single CPU. We ran the MCMC utility built into the INLA software for 2. 5 million iterations to obtain estimates of comparable accuracy, the analysis taking 40 h. Bayesian GLMMs 409 Downloaded from http://biostatistics. oxfordjournals. org/ at Cornell University Library on April 20, 2013 Fig. 4. Prior summaries: (a) ? 1 , the standard deviation of the spline coefficients, (b) effective degrees of freedom associated with the prior for the spline coefficients, (c) effective degrees of freedom versus ? , (d) ? 2 , the standard deviation of the between-individual random effects, (e) effective degrees of freedom associated with the individual random effects, and (f) effective degrees of freedom versus ? 2 . The vertical dashed lines on panels (a), (b), (d), and (e) correspond to the posterior medians. Table 2. REML and INLA summaries for spinal bone data. Intercept corresponds to Asian group Vari able Intercept Black Hispanic White Age ? 1 ? 2 ? REML 0. 560  ± 0. 029 0. 106  ± 0. 021 0. 013  ± 0. 022 0. 026  ± 0. 022 0. 021  ± 0. 002 0. 018 0. 109 0. 033 INLA 0. 563  ± 0. 031 0. 106  ± 0. 021 0. 13  ± 0. 022 0. 026  ± 0. 022 0. 021  ± 0. 002 0. 024  ± 0. 006 0. 109  ± 0. 006 0. 033  ± 0. 002 Note: For the entries marked with a standard errors were unavailable. 410 Y. F ONG AND OTHERS 6. D ISCUSSION In this paper, we have demonstrated the use of the INLA computational method for GLMMs. We have found that the approximation strategy employed by INLA is accurate in general, but less accurate for binomial data with small denominators. The supplementary material available at Biostatistics online contains an extensive simulation study, replicating that presented in Breslow and Clayton (1993). There are some suggestions in the discussion of Rue and others (2009) on how to construct an improved Gaussian approximation that does not use the mode and the curvature at the mode. It is likely that these suggestions will improve the results for binomial data with small denominators. There is an urgent need for diagnosis tools to flag when INLA is inaccurate. Conceptually, computation for nonlinear mixed effects models (Davidian and Giltinan, 1995; Pinheiro and Bates, 2000) can also be handled by INLA but this capability is not currently available. The website www. r-inla. rg contains all the data and R scripts to perform the analyses and simulations reported in the paper. The latest release of software to implement INLA can also be found at this site. Recently, Breslow (2005) revisited PQL and concluded that, â€Å"PQL still performs remarkably well in comparison with more elaborate procedures in many practical situations. † We believe that INLA provides an attractive alter native to PQL for GLMMs, and we hope that this paper stimulates the greater use of Bayesian methods for this class. Downloaded from http://biostatistics. oxfordjournals. org/ at Cornell University Library on April 20, 2013 S UPPLEMENTARY MATERIAL Supplementary material is available at http://biostatistics. oxfordjournals. org. ACKNOWLEDGMENT Conflict of Interest: None declared. F UNDING National Institutes of Health (R01 CA095994) to J. W. Statistics for Innovation (sfi. nr. no) to H. R. R EFERENCES BACHRACH , L. K. , H ASTIE , T. , WANG , M. 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Three Reasons Browsers Difficult To Secure â€Myassignmenthelp.Com

Question: Discuss About The Three Reasons Browsers Difficult To Secure? Answer: Introduction Today everyone knows about the web browser and its usage. To use web browser no need to do so much by a user. After installing web browser through its executable file, an icon appears on the screen and by clicking on that icon browser will open and user can use that accordingly. While using web browsers, a user has to face some security related issues that are critical enough to control. In next segment of report, I will discuss main challenges, problems, appropriate technologies to resolve issues and unclear areas of web browser attacks. Discussion The very popular web browsers are Mozilla Firefox, Internet Explorer, Google Chrome, Opera and Safari. These all browsers have to encounter problem of web attacks and these attacks are conducted by using social engineering, cross-site scripting and client-side exploits. Due to these attacks of web browsers, following challenges have to face by users. Challenges of Web Browser Attacks According to analysis, it is found that near about 45% of people those are surfing internet are not using most secure version of web browser and facing following challenges. The first challenge is of security patches applied to web browsers. If a web browser is not equipped with appropriate security patches than web browsers can result in vulnerable attacks. Moreover, security patches cannot work without proper patching of browser plug-ins (Zeltser.com, 2017). Another challenging factor is poor coding of web applications and vulnerabilities that are found in software solutions connected with web browsers. These weak factors of web browsers are necessary to overcome at developers end because hackers are taking advantage from these weak factors. Therefore, these above discussed challenges of web browsers attacks are required to control by developers by using advanced techniques at priority basis. Otherwise, issues of data lost and virus attacks will be increased. Problems of Web Browsers Attacks Due to above challenging factors of web browser attacks, various problems have raised for people who use web browsers that are listed as below: The first problem is that due to lack of security on web browsers, hackers can easily hack them and can access information about saved login, cookies, cache and visited websites. Another problem is of addition of harmful programming scripts into browsers by hackers. These scripts are vulnerable enough and redirect the users automatically to unknown websites without any knowledge of users (Shema, 2012). Those websites may have some malicious programs that can enter into computers system of user and can cause damage. Another problem is related to alteration of web browsers by malicious attacks from hackers side. These alternations lead to weird activities of web browsers that users are unable to understand (Lifewire, 2017). Relevant Technologies Web browsers can be protected from vulnerabilities by using appropriate technologies. Following are some relevant technologies that can be used to manage web browser attacks: The configuration of web browser should be done properly. While configuring browser, it is necessary to follow-up every instruction and security patches and plug-ins should also be installed properly. These plug-ins are helpful to protect web browsers from unknown entities (Us-cert.gov, 2017) Another relevant technology is to update browser regularly and also install third party solutions like ad-blocker to restrict harmful online advertisements to enter into the system. The advertisements so much injurious for system that these can slow down the system and can corrupt its boot files. Ad blockers are useful enough to stop these types of advertisements and other unknown entities. Web browsers have security and privacy options and with the help of these options web browsers can become more secure. For example, by hiding saved login passwords, by clearing caches and cookies that are not required at regular basis and by clicking checkbox options those are related to privacy of web browser content and its display. Application of Relevant Technologies The application area of these technologies is wide enough and these can be used to protect different versions of web browsers. Most of the web browser developers are using these relevant technologies to protect their browsers from unknown vulnerabilities (Howtogeek.com, 2017). Unclear Areas of Web Browser Attacks Web browser attacks are common among people but there are some vague areas about which people must have knowledge. The first thing that is not yet cleared about web browser attacks is that why programming languages are not so strong to restrict vulnerable browser attacks. At developers end this thing must be cleared that how programming languages can be more protective and strong to stop hackers to mischief web browser. Moreover, an appropriate solution for protect web browsers from hackers is also not properly found yet (Thorpe, 2017). Research Questions While analyzing problem of web browser attacks, some common research questions are found that are required to discuss here in this report. Question 1: What is the best way to get prevention from Web Browser Attacks? Answer: The most appropriate way to get prevention from web browser attacks is its periodical updates. Regular updates provides strength to browser to cope up with vulnerable objects (Computing Browser, 2017). Question 2: What is the main reason of Web Browser Attack? Answer: The entrance of any third party application or any other entity into web browser without knowledge but with permission of users, is the main reason of web browser attacks (Its.ucsc.edu, 2017). Described Issues in the Forum To know about the main issues of Web browser attacks I have selected a forum and according to that forum the main issues of web browser attacks are violation of security of users information such as cookies and login information, entrance of virus into system through web browser and slow performance of web browser as well as computer system. These issues put bad impact on users activities that they perform by using web browsers. This available information in forum is accurate and users of web browsers must have knowledge about these issues and must use appropriate security techniques to resolve these problems (SearchMidmarketSecurity, 2017). Issue that is addressed the in Forum All the mentioned issues in selected forum about web browser attacks are relevant. But another issue that is not addressed in this forum is that downloading content from different websites by using web browsers can also be harmful for users if it will not be scanned properly before downloading into system (Adams, 2017). This content may have some attached virus entities that enters into web browser easily and also try to corrupt the main files of browser. Due to this, browser does not work properly and also becomes slower than its normal speed. This issue is important to discuss here because nowadays, downloading rate of movies, audio and other content have become so higher and people do not care while downloading content and click on links that are harmful (Happyhamstercomputers.com, 2017). Impact of Issues of Real World The above discussed issues and challenges of web browser attacks have bad impacts on users of web browsers. This is because due to these attacks their computer systems, its content and web browser related information are at high risk. From the first day after installation, it has become essential for them to protect their web browsers by using secure software solutions that are especially made to get prevention from web browser attacks. If these software solutions will not be used then overall problem of web browser attacks can get worse. At developers end, due to improper management of security and privacy of web browsers, hackers are trying to get into browsers to violate them (Searchsecurity.techtarget.com, 2017). Most important Lesson Learnt from Discussions First of all I got to know that security and privacy of web browser is mandatory to maintain at users and as well as developers end. Moreover, users must not allow any unknown entity to enter into system through browser and to do this, appropriate third party software solutions should be used. On other side, while developing web browsers, developers must use secure programming languages and all security patches and plugins should be configured properly (Google, 2017). Conclusion In conclusion, it is right to say that without web browsers it is difficult to access data from different websites and to explore other internet sources. But security should be maintained at priority basis and everyone should have knowledge about possible challenges and risk factors of using web browsers and how these can be overcome by using security tools. References Securing Your Web Browser. (2017). Us-cert.gov. Retrieved 28 September 2017, from https://www.us-cert.gov/publications/securing-your-web-browser How to avoid attacks that exploit a Web browser vulnerability. (2017). SearchMidmarketSecurity. Retrieved 28 September 2017, from https://searchmidmarketsecurity.techtarget.com/tip/How-to-avoid-attacks-that-exploit-a-Web-browser-vulnerability Three Web Attack Vectors Using the Browser. (2017). Zeltser.com. Retrieved 28 September 2017, from https://zeltser.com/web-browser-attack-vectors/ Computing, H., Browser, W. (2017). What is a Web Browser?. WhatIsMyIPAddress.com. Retrieved 28 September 2017, from https://whatismyipaddress.com/web-browser Thorpe, E. (2017). Three reasons why browsers are so difficult to secure. IT PRO. Retrieved 28 September 2017, from https://www.itpro.co.uk/security/29077/three-reasons-why-browsers-are-so-difficult-to-secure Do You Know What a Web Browser Actually Is?. (2017). Lifewire. Retrieved 28 September 2017, from https://www.lifewire.com/what-is-a-browser-446234 7 Ways to Secure Your Web Browser Against Attacks. (2017). Howtogeek.com. Retrieved 28 September 2017, from https://www.howtogeek.com/228828/7-ways-to-secure-your-web-browser-against-attacks/ Web browser security news, help and research - SearchSecurity. (2017). Searchsecurity.techtarget.com. Retrieved 28 September 2017, from https://searchsecurity.techtarget.com/resources/Web-Browser-Security Google, i. (2017). 20 Things I Learned About Browsers and the Web. 20thingsilearned.com. Retrieved 28 September 2017, from https://www.20thingsilearned.com/en-GB/all/print Shema, M. (2012). Hacking web apps. Amsterdam [u.a.]: Elsevier/Syngress. Web Browser Secure Settings. (2017). Its.ucsc.edu. Retrieved 28 September 2017, from https://its.ucsc.edu/software/release/browser-secure.html Adams, D. (2017). 11 steps to reduce the risk of web attacks - Patriot Technologies, Inc.. Patriot Technologies, Inc.. Retrieved 28 September 2017, from https://patriot-tech.com/blog/2011/02/04/11-steps-to-reduce-the-risk-of-web-attacks/ Understanding Web Browser Attacks | Get Certified Get Ahead. (2017). Get Certified Get Ahead. Retrieved 28 September 2017, from https://blogs.getcertifiedgetahead.com/understanding-web-browser-attacks/ The 2 Main Types of Web Browser Attacks | Happy Hamster Computers. (2017). Happyhamstercomputers.com. Retrieved 28 September 2017, from https://happyhamstercomputers.com/computer-repair-tips/the-2-main-types-of-web-browser-atta

Johnson Was Born On Aug. 27, 1908, Near Johnson City, Tex., The Eldest

Johnson was born on Aug. 27, 1908, near Johnson City, Tex., the eldest son of Sam Ealy Johnson, Jr., and Rebekah Baines Johnson. His father, a struggling farmer and cattle speculator in the hill country of Texas, provided only an uncertain income for his family. Politically active, Sam Johnson served five terms in the Texas legislature. His mother had varied cultural interests and placed high value on education; she was fiercely ambitious for her children. Johnson attended public schools in Johnson City and received a B.S. degree from Southwest Texas State Teachers College in San Marcos. He then taught for a year in Houston before going to Washington in 1931 as secretary to a Democratic Texas congressman, Richard M. Kleberg. During the next 4 years Johnson developed a wide network of political contacts in Washington, D.C. On Nov. 17, 1934, he married Claudia Alta Taylor, known as "Lady Bird." A warm, intelligent, ambitious woman, she was a great asset to Johnson's career. They had tw o daughters, Lynda Byrd, born in 1944, and Luci Baines, born in 1947. In 1933, Franklin D. Roosevelt entered the White House. Johnson greatly admired the president, who named him, at age 27, to head the National Youth Administration in Texas. This job, which Johnson held from 1935 to 1937, entailed helping young people obtain employment and schooling. It confirmed Johnson's faith in the positive potential of government and won for him a group of supporters in Texas. In 1937, Johnson sought and won a Texas seat in Congress, where he championed public works, reclamation, and public power programs. When war came to Europe he backed Roosevelt's efforts to aid the Allies. During World War II he served a brief tour of active duty with the U.S. Navy in the Pacific (1941-42) but returned to Capitol Hill when Roosevelt recalled members of Congress from active duty. Johnson continued to support Roosevelt's military and foreign-policy programs. During the 1940s, Johnson and his wife developed profitable business ventures, including a radio station, in Texas. In 1948 he ran for the U.S. Senate, winning the Democratic party primary by only 87 votes. (This was his second try; in 1941 he had run for the Senate and lost to a conservative opponent.) The opposition accused him of fraud and tagged him "Landslide Lyndon." Although challenged, unsuccessfully, in the courts, he took office in 1949. Senator and Vice-President Johnson moved quickly into the Senate hierarchy. In 1953 he won the job of Senate Democratic leader. The next year he was easily re-elected as senator and returned to Washington as majority leader, a post he held for the next 6 years despite a serious heart attack in 1955. The Texan proved to be a shrewd, skillful Senate leader. A consistent opponent of civil rights legislation until 1957, he developed excellent personal relationships with powerful conservative Southerners. A hard worker, he impressed colleagues with his attention to the details of legislation and his willingness to compromise. In the late 1950s, Johnson began to think seriously of running for the presidency in 1960. His record had been fairly conservative, however. Many Democratic liberals resented his friendly association with the Republican president, Dwight D. Eisenhower; others considered him a tool of wealthy Southwestern gas and oil interests. Either to soften this image as a conservative or in response to inner conviction, Johnson moved slightly to the left on some domestic issues, especially on civil rights laws, which he supported in 1957 and 1960. Although these laws proved ineffective, Johnson had demonstrated that he was a very resourceful Senate leader. To many northern Democrats, however, Johnson remained a sectional candidate. The presidential nomination of 1960 went to Senator John F. Kennedy of Massachusetts. Kennedy, a northern Roman Catholic, then selected Johnson as his running mate to balance the Democratic ticket. In November 1960 the Democrats defeated the Republican candidates, Richard M. Nixon and Henry Cabot Lodge, by a narrow margin. Johnson was appointed by Kennedy to head the President's Committee on Equal Employment Opportunities, a post that enabled him to work on behalf of blacks and other minorities. As vice-president, he also undertook some missions abroad, which offered him some limited insights into international problems. Presidency The assassination of President Kennedy on November 22,

William Wentworth essays

William Wentworth essays The reason that I picked William Charles Wentworth was because he was very important to the way we live today in two ways, the first was opening up new grazing lands by going over the blue mountains, but most importantly the way the government system was set up to today. William Charles Wentworth was born in 1790 Norfolk Island and died in 1872. Wentworth was educated in England at the University of Cambridge. He studied law in Sydney. Wentworth is best known for crossing the Blue Mountains in 1813 and finding new grazing lands, but he should be best known for what he did for the convicts and the development of the government systems in Australian that are still used today. He also led the convicts fight to overcome the political supremacy exercised by government officials and voluntary settlers against them. In 1842 New South Wales became the first Australian colony to be granted representative government , largely as a result of Wentworths efforts. A member of the Legislative Council formed that same year; he helped obtain a formal constitution for the colony in 1854. Meanwhile, in 1850, he was responsible for passage of the bill founding the University of Sydney. He led the upper house of the Australian parliament in 1861. Although he wanted self-rule for British colonies, as a wealthy land owner he disapproved of the growing democracy in Australia which had made it one of the most advanced political systems in the British Empire, and in 1862 he settled in England. Wentworth is often called the Australian patriot. The first thing that William was known for and still today is best known for is being one of the first men over the Blue Mountains in 1813. He was a companied by William Lawson Gregory Baxland. It was a great discovery it took twenty-one-days, in treacherous land. William wrote the land was beautiful and rich, the boundless burst, till nearer seen beauteous landscape opning like canaan in rapt I...

Definition and management of service quality Essay

Definition and management of service quality - Essay Example According to the manager of the Chesthunt Hotel, service quality is based on the quality, customer satisfaction and identification of customer value as either important or very important or very important. Those companies that have high quality of services as well as goods had higher market share, higher return on investment and asset turnover than companies with perceived low quality.If consumers somehow become better customers -- that is, more knowledgeable, participative, or productive -- the quality of the service experience will likely be enhanced for the customer and the organization. Because it affects those factors, then it certainly affects customer satisfaction, the link of service quality with customer satisfaction, which is, the degree of fit between customer's expectations and perceptions of service.Based on the perception of the manager, customer criteria determine the definition of quality and the variables that affect perceptions of quality. They explained that variab les may change with circumstance, experience, and time. In addition, service providers may influence the variables that drive customer perception to service quality. The perception of the customers may also vary by circumstances, time, and experiences. He also explained that the total perceived value of a service comes from two sources. First, customers perceive value that originates from the service act itself; second, customers perceive value that originates from the quality of the service act. ... On the other hand, quality is much difficult to define as it depends on the perception of the consumer. Basically it is defined in terms of being transcendent, customer led, or value led. The provision of quality customer service is a multi-faceted concept as a number of factors must be met by the hotel in order to achieve it. To integrate quality in service provision, it is important that the hotel has the right skills, resources and values. As quality customer service is influenced by various factors, the involvement of both hotel management and the employees must be present; in the case of Chesthunt it has been made clear that training alone is not sufficient for service quality (Ghobadian,Speller and Jones, 1994,p. 43). The commitment, leadership and adaptability of the management towards change are also important for quality service. The values and skills of the employees on the other hand, must be prioritized as well. They must be given enough empowerment to contribute effectively towards customer satisfaction. The importance of quality in customer service has been recognized by Chesthunt hotel. The management of Chesthunt hotel have applied prioritized quality in customer services, resulting to positive business outcome. Customer satisfaction, loyalty, employee satisfaction and profit growth are some of the main advantages of this business practice. In order to cope with the present business challenges, Chesthunt hotel have implemented different strategies that will enhance their respective customer services. Consumer studies, trainings and application of information technology are some examples of the most commonly used strategies for customer services. Service delivery

Digital project management Essay Example | Topics and Well Written Essays - 2500 words

Digital project management - Essay Example Undoubtedly, the field of project management has as well been influenced by this innovative technology. Currently such highly refined online collaboration technology is used in the field of project management for remotely handling and managing the projects, directing business contracts as well as managing virtual relationships (Mumbi & McGill, 2007), (The Project Wall, 2011) and (Filev, 2008). This paper discusses the important aspects of digital project management. In this scenario I will assess the prime strategies and techniques which can be applied by a project manager in order to deliver the required results of the project. This paper will also discuss new developments and advancements in the field of project management. Project Management 2.0 This section discusses a new evolutionary paradigm of Project Management 2.0 that is evolved through the high-tech support and facilities of web 2.0. Project Management 2.0 (sometimes mistakenly acknowledged as the Social-Project-Managemen t) is one of the evolutionary information technology (IT) based project management practices. Additionally, this new IT based project management is implemented through the interface of new enhanced Web 2.0 tools and technologies. In this scenario, the modern Web 2.0 tools and technologies like wikis, blogs, shared communication boards, collaborative software, etc. have really supported the development of project management to a superior level. Additionally, through the implementation of these new Web 2.0 technologies, shortly distributed as well as globally distributed online virtual teams are competent to work in cooperation with a great deal of additional proficiency by using the next-age, usually less costly or free online development and project management systems. In addition, these innovative applications and effective tools completely change the customary approach of the project manager. Moreover, the new Project Management 2.0 practice demonstrates an impressive enhancement in the competency for collaboration or cooperation in distributed project management teams (Filev, 2008), (The Project Wall, 2011) and

Social Value of Scientific and Technological Enterprises Essay - 1

Social Value of Scientific and Technological Enterprises - Essay Example There are many types of social value associated with research. International Business Machines, for instance, has stated that â€Å"Our research needs not only to attract the attention of academia but also to have an impact on a wide range of sectors in society. Fortunately, IBM has various systems to utilize research results for the benefit of society† (Research Value to Society, 2008: n.p.). First, IBM intends to convert its research into products. This creates social value in the form of employment, increased tax revenues for social services, business stability and expansion, and a better standard of living. Second, though the research is protected by intellectual property rules, it does become disseminated in many ways as public knowledge. Although others may not violate the research protected the intellectual property laws, they may learn how to build on the newly discovered knowledge. Finally, research enterprises tend to be rather collaborative in modern times and this means that knowledge is being shared commercially and socially; as an illustration, IBM has stated that â€Å"IBM supports the promotion of open systems that optimize open standards and open sources with the goal of realizing collaborative innovations. TRL is working with governments and corporations to conduct research in open technologies, including open document formats (ODF)† (Research Value to Society, 2008: n.p.). The significance of the research is fundamentally the dissemination of knowledge which is most often commercially-oriented but which is increasingly being used to promote social values such as public health and safety and other social objectives.

Robert Brownings Poetry | Analysis

Robert Brownings Poetry | Analysis Compare the examination of abnormal psychology in Robert Brownings poetry, and in Iain Banks novel, The Wasp Factory. Make illuminating connections with the work of Edgar Allan Poe.   The abnormal mental state of the narrators in both Brownings poetry and in Banks novel, The Wasp Factory, is intrinsic in achieving the gothic style. Whilst the protagonists insanity is more implicit in Brownings poetry, the narrators, nevertheless, display similar characteristics of psychosis and delusion. Indeed, this madness disconnects the characters from the rest of society, and this element of monstrosity is vital in creating the intrigue and terror that ensues. Inclusion of such monstrous figures destabilises the natural order: it challenges the fixed social structures and ideology, and becomes inconsistent with what the majority considers both acceptable and intelligible. Yet, whilst on the surface gothic works may appear to reinforce these seemingly grotesque characteristics, in many respects, through exposing the unnatural, they deconstruct the illogical, and thereby attempt to create a set of social norms. The first chapter of The Wasp Factory, The Sacrifice Poles, serves as a warning to the reader that they are entering into the domain of Franks psyche. The unconventional behaviour she displays is evident through her intentional replacement of common nouns with proper nouns: for instance, the capitalisation of words such as Factory and Poles. Essentially this represents the objects which Frank views as significant in the private world that she has constructed for herself. Franks tendency to fantasise is further demonstrated through the naming of her catapult- The Black Destroyer. In fact, Frank goes beyond symbolism- for instance she assigns the house with humanistic attributes through personification: powerful body buried in the rock. Of course, this description may well be representative of the dark life she lives, in regards to both her social isolation and the sinister lifestyle that she leads. The conflicting behaviour that Frank exhibits, that is her seemingly child-like behavio ur and her meticulosity with rituals, underlines her highly unusual mental state. The initial lines of Porphyrias Lover similarly imply the protagonists unusual frame of mind. The use of pathetic fallacy and personification, for instance, the sullen wind is not only effective in creating a cold and melancholy atmosphere, but may be representative of the narrators mind; consequently, there is a strong sense of foreboding. The abnormal psychology of the narrator is further exemplified through the description of how the wind did its worst to vex the lake. Likewise, the wind is awake and tears down the elm-tops for spite. Thus, the wind is perhaps an emblem of the narrators destructive capacity: it could be argued that the lake is representative of Porphyria, and the wind is representative of the narrators anger towards Porphyria. In this sense, the narrators anger is possibly a consequence of his inability to possess the femininity that Porphyria exudes. The Laboratory also reveals a narrator that exhibits an unstable mental state. The anapaestic meter of the poem po ssibly reflects her enthusiasm and engagement in producing the poison. Additionally, the tricolon Grind away, moisten and mash up thy paste is representative of her increasing exhilaration as the poison approaches completion, whilst active verbs such as grind and pound convey violent connotations, which present us with an ambience of foreboding. The exquisite blue and the gold oozings of the poison, however, are possibly an allusion to the opulence of the French court. There is a stark contrast between the murky laboratory, which is arguably representative of the decadent aristocrats, and the affluence of the court; this is perhaps symbolic of the widespread corruption that encompassed the French aristocracy. During the emergence of the gothic literary movement, history was characterised by widespread political unrest often resulting in revolution. Subsequently, the genre became very popular with writers as it enabled them to express sympathy and moral concern over such movements. I n The Fall of the House of Usher, Poes imagery describing the attrition of the house is perhaps an attempt to symbolise the narrators degenerating mental state. Also, the Haunted Palace that is occupied by evil things (that) assailed the monarchs high estate is possibly an allusion to how his mind is being possessed by the malevolent forces that ostensibly surround the house. In The Wasp Factory, Franks father also displays an abnormal state of mind, which is demonstrated through his efforts to exert constant authority over his daughter. Mr Cauldhame has ultimately left Frank excluded from society through his decision to conceal his identity and home educate him. More sinisterly, however, Angus, through experimentation, has essentially created a contemporary Frankenstein. Fundamentally, Angus has suppressed Franks innate feminine characteristics through experimental hormone therapy and has indoctrinated her with misogynistic views. This enables Mr Cauldhame to think that he is in control of what he views as the correct father- son relationship. Of course, normality has no association with Franks life: the child-like mentality that she exhibits through her fantasy, perhaps signifies that, in reality, Frank is scared of the real world in a multitude of ways. Alternatively, this fantasy world may keep Frank at least partially sane: Eric shows the stark conse quences that may result from the real world. Moreover, their use of imperial measurements is not only indicative of Mr Cauldhames compulsive disorder, but accentuates the concept that the island does not progress with time. In this respect, the Cauldhame family is a microcosm of the demise of the empire and the island is a last remnant of it. Accordingly, it can be argued that it was the demise of Angus position as a patriarch that has ultimately brought about his decision to devise an all male enclave. Angus obsession with control, therefore, stems from his fear of being replaced as the monarch of the empire because of the emergence of the new feminist movement. Thus, Angus Cauldhames behaviour is synonymous to the description found in Jerrold Hodges gothic textbook: Angus has created a patriarchal enclosure designed to contain and even bury a potentially unruly female principle'. The way in which Banks presents the reader with a typical boys story whose protagonist is, in truth, a girl is perhaps a critique of the way in which society devises fixed binary gender stereotypes, and thus is an attempt to undermine these traditional gender expectations. Frank, however, conforms to the typical gothic female character, who is suppressed by a domineering male; the irony is that Frank is both the subjugated female and the tyrannical male. A similar desire for control is displayed by the narrator in Brownings My last Duchess. This element of control, that the narrator wishes to possess over his wife, is exemplified through the poems iambic pentameter. With twenty-eight rhyming couplets, the very tight structure of the poem is possibly representative of the level of authority and control that he expects to exert over his wife. The curtain that he has drawn over his late wifes picture is again perhaps symbolic of the level of authority that he desires to exercise over his female partners. Indeed, he gave commands; Then all smiles stopped together. The underlying sense of threat signifies his expectations of how his wife should behave. Ironically, however, the Duke can only, when his wife is dead, counteract what he perceives as her earnest glance. Fundamentally, his wife has been objectified from subject to object; she is simply one of his possessions. Similarly, the narrator in Porphyrias Lover demonstrates a notion of control. The sibilance in the sentence, she shut the cold out stresses how she is able to alleviate the narrators mental anguish. However, it also stresses the narrators dependency on Porphyria and this concept is reiterated through the way she was mine, mine. The use of repetition thus highlights the possessive nature of the protagonist. Certainly, it is possible that the narrator is resentful of both her social superiority and of her more commanding presence. In the nineteenth century, society was characterised by patriarchal codes, which women had to adhere to; men typically exerted absolute control over their female partners. Thus, Porphrias gay social life may also be a source of the protagonists bitterness and the only way to free himself of such powerlessness is to kill her. Browning may be attempting to indicate a reversal of gender roles; the male is the weak character through his inability to keep control of himself- let alone Porphyria. In this sense, the protagonists obs ession with maintaining control is similar to that displayed by Mr Cauldhame in The Wasp Factory. Franks aggressive behaviour also illuminates her abnormal psychology. In many ways, the buck, which Frank encounters, is symbolic of all the things that she wishes to possess: that is, ironically, an alpha-male persona. This concept of masculinity is maintained through the way that Frank hissed. This animalistic imagery, once again, highlights Franks aggressive and territorial nature, which reveals her very apparent abnormal mindset. In essence, though, this encounter is an externalisation of Franks internal battle. This externalisation of an internal conflict is perhaps representative of Franks struggle with her dual gender identity. Additionally, this attack of revenge on the buck reinforces that Frank has the capability to kill and in fact clarifies her monstrosity. More disturbing, however, is Franks admittance that it felt good; this compounds her mental disposition. This scene provides the reader with a very clear image of Franks ability to inflict suffering and destruction whi lst chillingly deriving pleasure out of it. The externalisation of internal conflicts is equally manifested in Poes work. For instance, in The Tell-Tale Heart and The Black Cat the narrators attempt to bury the corpse symbolises their attempts to conceal the problem. In The Black Cat, the narrators attempt to hide the corpse under the wall is ultimately representative of his desire to contain his problems within. Alas, for the narrators, their failure to deal with their problems effectively, leads to the resurfacing of the initial problem, and, inevitably, their downfall. However, despite Franks seemingly grotesque and in many ways nauseating behaviour, the reader can, nevertheless, sympathise with her. Franks manipulative nature may well be an attempt to expose her abnormal mind further. However, an encounter with this element of monstrosity is sometimes known to provoke paradoxical emotions. This notion of abjection as Julia Kristeva describes is the in-between, the ambiguous, the composite. Thus, the monstrous element has the ability to induce sentiments of horror and desire, disgust and fascination. Indeed, Franks mix of monstrosity and humanity possibly provide us with a forewarning of the transgression of which we may all be capable of; this, of course, presents a poignant and unsettling dimension. The Inclusion of animals is evident in Franks encounter with the buck, and in Poes The Black Cat. Poes story, like Banks novel, perhaps includes these animalistic aspects to reiterate that by undertaking such vicious acts the narrators are in complete deficiency of a logical human psyche, and are more comparable to animals who ultimately do not work within such moral frameworks. The authors are perhaps attempting to demonstrate that the narrators are deficient in human ethics: as philosopher Daniel Dennett states, many regard human ethical knowledge as a marvellous perspective that no other creatures have. The unconventional behaviour displayed by the narrator in Porphrias Lover, is implied further through the way he debated what to do. This uncertainty accentuates that when he kills Porphyria, it is a conscious decision and not an impulsive act. The composure, which the narrator exhibits is also shown through the very orderly ABABB rhyme scheme which is ultimately suggestive of the attitude, albeit this makes him appear all the more dangerous. However, alliteration in the sentence Blushes beneath my burning kiss presents a degree of desire for Porphyria. The paradox may nonetheless simply epitomise his psychosis. In The Wasp Factor, Franks casual admittance that his killings were Just a stage (he) was going through, stress his lack of remorse; in fact, like the narrator in Porphrias Lover, Frank is essentially justifying his actions. Hence, it reveals the very apparent psychosis of both narrators. In addition, despite Brownings clues towards the protagonists madness, it is never evide nt through the tone or diction of the poem. Instead of being presented with a stereotypical mad character, like Eric in The Wasp Factory, it is more implicitly implied. Alternatively, his madness is suggested through what the narrator does not say and the fact that he perceives Porphyria as being happy and at peace: The smiling rosy little head; the narrators portrayal of events can simply not accord with reality. Undoubtedly, the narrative of Porphrias Lover could well be a figment of the protagonists imagination; if this is the case, then it clearly reinforces that the narrator exhibits an element of abnormal psychology. The concept of the narrator justifying their actions is illuminated in The Tell-Tale Heart. The narrator is essentially justifying the murder of the old man through the notion that he had an evil eye: I think it was his eye!- yes, it was this! In essence, the narrators uncertainty alludes to the concept that it is simply an attempt to justify the sinister and irra tional behaviour that the reader is about to witness. A parallel can be drawn between the way in which the narrators justify their behaviour and the notion of self-deception. In The Wasp Factory, Franks self-deception is exemplified through the way in which she has essentially created her own fantasy. Franks propensity to self-deceit is apparent through the final chapter: the factory was my attempt to construct life, to replace the involvement which otherwise I did not want. Moreover, the level of deception is explicitly expressed through her engagement in rituals, which is an attempt to affirm her position as man. Franks repetition of the secret catechisms thus helps her to create the illusion of her male persona. Ultimately, though, her attempts are futile: the juxtaposition of the bowie knife and comb that Frank carries around presents the reader with a subtle intrusion of Franks real gender identity. These two contrasting objects possibly symbolise Franks conflicting personality: the knife is representative of the destructive behavi our that she asserts to conform to her male persona, whilst the comb is representative of her inherent, albeit more restrained, feminine character. This lingering uncertainty regarding sexual identity, as Boris Kà ¼hne argues, is a source of the uncanny and presents us with a pervasive gothic feeling; this ostracises Frank from societal norms and is inevitably the major source of her monstrosity. This is also evident in Brownings Soliloquy of the Spanish Cloister. Essentially, the narrator soliloquises his own inadequacies and attributes them to Brother Lawrence. Stanza four illustrates the narrators perception of his own self-righteousness, and indeed his dedication to denouncing Brother Lawrences commitment to his faith. The narrator describes Brother Lawrences ostensible lusting over the two nuns, Dolores and Sanchicha. Yet he goes on to explain that that is, if hed let it show; crucially, there is no evidence that Brother Lawrence has been looking at the nuns lecherously. Rather, the detailed account of the nuns activities must be a product of the narrators own impure thoughts, and his attempts to attribute these unchastely thoughts to Brother Lawrence can only serve to accentuate his self-deceptive and manipulative personality. The monks attempt to describe himself as the epitome of morality continues with his comment regarding the symbolic divide between their table eti quette. The crossing of his silverware, the narrator argues, symbolises his remembrance of Christs death on the cross; Brother Lawrence displays no such gesture. Additionally, the narrators absurd suggestion that Brother Lawrences drinking of the watered orange pulp in three sips supposedly denies the Arian doctrine again provides us with an illustration of his attempt to reaffirm his moral superiority. Ironically, despite the narrators belief, his attempt to condemn Brother Lawrence into eternal damnation reiterates his spiritual inferiority; this irrational behaviour provides an indication that Brownings narrator also exhibits an elementary characteristic of abnormal psychology. The quasi-religion that Frank constructs evidences the depth of her delusion and, correspondingly, her abnormal psychology. However, Franks religion has not stemmed from an intrinsic religious belief, but arguably out of a necessity to harbour some control, whilst denying any element of responsibility. Frank, in light of the failure of familial relationships, relies on The Wasp Factory to guide and ironically protect her. Frank creates a polytheistic religion: water, fire and death are all pseudo-Gods and perhaps compose Franks trinity. Indeed, Franks monstrosity is a result of her moral indifference. Since sea has destroyed what (she has) built wiping clean the marks (she) made Frank perhaps deduces that this permits her to inflict suffering on animals, which are below the pseudo-hierarchical order that she has constructed. However, the contrast to the sea destroying her dams and the sadistic killing of the rabbit is not apparent to Frank. Franks quasi-religion naturally has many Ch ristian elements: the lighting of the candles in Franks religions, nevertheless, contrastingly symbolises a destructive power. Banks notes that this was an attempt to satirise religion, and expose the ways in which we are all deceived, misled and harking back to something that never existed. Consequently, Banks ridicules all religions perhaps in a bid to create a society that is free from religious doctrine, and one that advocates logic and equality. Poes work also contains religious undertones. For instance, in The Tell-Tale Heart, the narrator essentially ascribes himself the role of God; this is reinforced by the way he describes the extent of my powers- of my sagacity. The delusion of grandeur ultimately reveals his damaged psychological state. Religious overtones are similarly apparent in Porphyrias Love. The imagery arguably possibly portrays Porphyria as an angelic entity. The way she glided in and her ability to make the cottage warm suggest a supernatural quality, with her yellow hair and bare white shoulder possibly alluding to her angelic purity; even when Porphyria is dead, the narrator describes her blue eyes without a stain. The presentation of Porphyrias purity and innocence may well be an attempt by Browning to accentuate a feeling of anguish after Porphyrias death. Conversely, the magical element that the narrator has ascribed to her may ultimately be a result of the magic in his head. In this respect, the reference to her eyes, which were without a stain, is perhaps his warped perception that Porphyria worshipped him; after all, the eyes are a window to the soul. Certainly, the notion that she worshipped him is reinforced by his absurd insistence that she is happy and at peace in his arms: the smiling rosy lit tle head. The fact that God has not said a word, however, is perhaps a direct attack on God: a sin has been committed yet no justice has been obtained. Indeed, Brownings poem was written during the Age of Enlightenment, a time where the legitimacy of the Bible was challenged and an emphasis of rationalism over religion occurred. In a rather different perspective, the God which is referenced may simply be a rhetorical God, which the narrator uses to convey his perception of how any God across all religious spectrums would view the strangulation of Porphyria as morally correct; this would clearly reinforce that the narrator exhibits an abnormal mental state. To conclude, all the texts examined contain quintessential characteristics of gothic mode and symbolism, which disclose the abnormal psychology of the narrators. The monstrous aspect pervades us with a feeling of uneasiness and revulsion. Yet, through including the seemingly grotesque and disconnected narrators, the gothic is able to defuse the transgressive, and challenge the conventional expectations of society. In The Wasp Factory, Banks perhaps attempts to satirise the way in which society constructs binary gender stereotypes and, in doing so, challenges what appears to be an illogical social norm. Similarly, Brownings Porphyrias Lover and My Last Duchess, through including subjugated female characters, possibly battles to expose the patriarchy that characterised Victorian society. Poes narrator in The Fall of the House of Usher, perhaps similar to Frank in The Wasp Factory, possesses a dual persona, or doppelganger, which accentuates the transgressions of which all humans may be capable of. In this way, through exposing the unnatural, the gothic advocates rationality and, as Kà ¼hne argues, acts as final safeguarding device against the invasion of the monstrous in the readers actual life.