Ethanol Fuel Essay Example for Free

Ethanol Fuel EssayEthanol is regarded as an attractive alternative to natural gas and other fossil fuel-based automotive energy sources because they can assist in ending dependence on foreign-based oil imports, a dependence which reaches 140 one thousand million gallons a year in the United States alone.Gal Luft, a director of the Institute for the Analysis of Global Security, asserts that oil dependence is problematic not just for scotch reasons, yet for political ones as well, maintaining that the relationship between the United States and the oil-producing Muslim nation states is strained at best (as well as noting that this political tensity is probably of greater concern than any purported political instability are said to plague these oil-producing nations. )As such, oil dependency constitutes a political liability. Furthermore, geologists attain argued that oil prices are not going to get any better, what with the increasing cost of drilling current oil reserves, and ethanol is therefore an economically operose alternative.However, ethanol is not without its critics. Critics charge that the ethanol boom in the United States, which relies primarily on corn for feedstock has resulted in dramatic increases in food prices (and it is worth noting that many non-corn based food commodities utilize corn syrup). Farmers have begun to see the ethanol boom as lucrative incentive for corn production, which in its close positive sense has raised their incomes and given new hope to flagging rural economies.This translates to a diversion of grain-based agriculture towards fuel production, imposing dramatic impacts upon the costs of maintaining food supply for both the worlds hungry and the worlds well fed. Grunwald maintains that the grain it takes to fill an SUV tank could feed a somebody for a year.Furthermore, research presents that the rapid expansion of corn agriculture to feed the ethanol boom holds environmental consequences. Environmental journalis t Richard Manning charges that industrialized agriculture is evil to soil fertility.Mindy Lubber concurs, maintaining that massive land conversion of lands towards the production of corn could recreate the conditions of The Great Dust Bowl, a period in the American heartland which saw hundreds of thousands of would-be wheat farmers plow the soil to death to profit from golden grain.However, ethanol proponents are careful to remind us that it is also a renewable fuel source. Because it is derived from grain and other starch crops, and may also be obtained from cellulosic biomass such as crop residue, abrasion cane bagasse and old newspapers, it is essentially a sustainable resource insofar production is concerned. Additionally, its energy potential, while being significantly less than that of gasoline, is endowed with a high octane level that gives it the power that is all important(p) to the operation of high compression engines such as those found in high performance automobile s.Furthermore, Khosla argues that the trajectory of ethanol development will result in continuously increasing potential for energy density and engine efficiency that would rival that of gasoline. While opponents of corn-based ethanol have charged that the energy yield that exceeds the amount of energy used in its production, developments in cellulose-based ethanol have been promising, which may lead to a future in which the biomass and waste of any municipality could be used for ethanol production.Beyond the direct effects on corn prices, the corn-based ethanol boom also affects the price of various food commodities. Grunwald notes that the soybean grocery is affect to such an extent as to jack up the price of soybeans.Also, increased production of sugarcane-based ethanol, combined with sugar quotas in the U.S. ensure that domestic prices of sugar continue to inflate. As such, producers of high fructose corn syrup such as the agribusiness giant Archer Daniels Midland benefits in a bit where the price demand for high fructose corn syrup remains inelastic, simply because they are able to charge more for without fearing that sweetener-dependent companies will retaliate by switching to sugar.Even the price of a Starbucks latte is not immune to the effects of the corn-based ethanol boom, as diversion of crop grown to fuel production takes it out from the mouths of dairy cattle.Furthermore, the demand for ethanol has effects that reach as far as the Amazon rainforest, where the resulting expansion is leading to its deforestation. This is an overwhelmingly negative development as the rainforest is a highly biodiverse region. Grunwald reports that scientists believe that this could essentially reduce the Amazon to a savanna, or worse, a desert.Ethanol is not just an alternative fuel, but a complex distillation of political, economic and environmental issues. As such, it is difficult to reduce it to a simple either/or issue, let alone okay it wholeheartedly as th e silver bullet that will solve problems of climate change and fossil fuel dependency.

Montessori Senstive Periods Essay Example for Free

Montessori Senstive Periods EssayDefine the term sensitive periods, and explain how the t from each oneers intimacy and consciousness of these periods determines his / her preparation and custodianship of the inclined(p) environment A thorough understanding and knowledge of the sensitive periods (Montessori, 1972) that a electric razor passes through is crucial in aiding the instructor to provide a suitable environment to assist a boors optimum break inment as the environment that the child is in plays a crucial component part to their formation according to Montessori. A sensitive period refers to a special sensibility which a creature acquires in its infantile state (Montessori, 1966, p fester 38). such(prenominal) sensitive periods were first discovered in insects by the Dutch scientist Hugo de Vries. Montessori stated that sensitive periods can also be found in children and are very important for a teacher to consider to aid optimum development through providing the correct type of environment. Therefore to fully understand the sensitive periods, an understanding of Montessoris periods of growth (Montessori, 2007) that she devised from birth to maturity is needed. These outlined the progress of a childs mental development, which correspond with phases of physical growth.Montessoris periods of growth consist of phase one birth to six whatever years, phase two six to twelve years, phase three twelve to eighteen years and phase quaternary eighteen to twenty-four years. Within each stage the child has different characteristics and needfully, therefore requiring an alternative suitable environment for optimum development to occur, as each stage lays the foundations for the child to continue to develop within the next stage, with the first stage being fundamental (www. casadimir. org). Stage one is split into two sub-phases, comprising of birth to age three and three to age six.During this first stage the child has an absorbent judging (Montessor i, 2007). The unconscious absorbent straits from birth to three years old, which is described as a mind that is constantly absorbing impressions from the environment and yet does this without knowing that it is doing so, and without willing it (Standing, 1998, summon 109). The conscious absorbent mind from three to six years continues to build on the faculties that developed during the phase of the unconscious mind. However, instead of soaking in the environment unconsciously it is the childs own ego which guides and directs him (Standing, 1998, age 112). This stage is critical for the foundations of an all elaborate well developed child both mentally and physically. Stage two, from six to twelve years, is a stable periodwith noticeable physical and mental changes. The absorbent mind is replaced with reasoning and logical thinking the child continues to develop and build upon his development from stage one. Stage three, twelve to eighteen years, is split into two sub-phases compr ising of twelve to cardinal years, the stage of puberty and fifteen to eighteen years, the stage of adolescence.This period is a very unstable and an emotional time for the child. Stage four, eighteen to twenty four years, the beginnings of adulthood this time is usually very stable. Montessori identified six main sensitive periods, consisting of a period of sensibility to order, to language, for refinement of the senses, for walking and movement, for elflike disapproves and to the genial aspects of life, all of which are present during the first stage of growth the absorbent mind. Theses sensitive periods are not consecutive some are continuous and some overlap, which will now be looked at in greater depth.The period of sensitivity to order is one of the first sensitive periods to been seen in the development of a child and usually appears within the childs first month. Throughout this sensitive period the child requires a precise, ordered environment, which can be observed by the rejoice which children show at seeing things in their correct places (Montessori, 1972). The presence of this sensitive period however, is even more evident when the order is somehow interfered with.For example, in the mysterious of Childhood (1972), Montessori describes the agitation of a 6 month old child when a sunblind was placed upon the table by a woman ledger entry the room the child immediately began to cry. The woman automatically presumed that the child wished to play with the parasol, this brought on further frustration causing the little girl to push the parasol away when it was given to her. Her mformer(a) suddenly unfeignedised and when the parasol was removed from the room it was tho then that the child became calm again.In Montessoris words the object out of place had violently upset the little girls pattern of memory as to how objects should be arranged (Montessori, 1972, page 50), thus causing the child discommode demonstrating the need for a precise en vironment. Another example illustrated by Montessori in the Secret of Childhood (1972), was when she was on a guided tour through the burrow of the Grottoof Nero in Naples, when a mother carrying a little boy took her coat off and threw it over her arm while she continued to carry the child. The child one time again became agitated and ceaselessly cried.Various approaches were used to try and sooth the child, all of which were unsuccessful, until Montessorisuggested that the mother should put her coat on. Following this action the childs tears disappeared and he continuously said Coat shoulder (Montessori, 1972, page 51), the child once again smiled as he felt that his mother had eventually understood him. With these examples and the knowledge of the childs sensitivity to order it demonstrates the need for the teacher to ensure a well ordered environment that is predictable to the child, while allowing sufficient freedom and time for the child to explore ontheir own.The period o f sensitivity for language occurs between birth and approximately five years old. During this time the child masters basic sentence patterns in their nativelanguage and goes on to develop more complex sentences during the next stage of growth. From the moment the child is born, without lessons and without conscious effort he learns to pronounce the language he hears almost him with nonsuch (Standing, 1998, page 121), simply by listening to the languagewords spoken slowly and clearly. The child listens voluntarily then goes on to utter sounds, experiencing vibrations and developing his oral muscles.Montessori (1972) stated that the childs sensitive period for language tends to pass unnoticed, as the environment he is in, surrounded by adults speech provides the necessary requirements for his development. This sensitive period is only acknowledged when the child demonstrates their joy through smiles and bodily movements, when tuned into the sounds of language. The period of sensiti vity for the refinement of the senses, begins approximately when a child begins to crawl and can truly see to it and experience his environment, this continues generally until the age of eight.Through guided teaching using the correct material in an appropriate environment and practice, the childs senses can be improve so that the child can distinguish between minimaldifferences. Using the colour box to distinguish between gradients of colours or grading the sound cylinders is a good example of this. The period of sensitivity for walking and movement, occurs approximately at the age of one when the child first begins to walk. Although they walk slowly and with no real rhythmic step or goal, the child can walk for long periods of time covering great distances, showing thorough enjoyment during their travels (Lillard, 1972).This allows time for a child to explore the extracurricular world with the freedom of a suitable large space, improving his physical coordination and fulfilling his desire to absorb his surroundings. The period of sensitivity for small objects becomes apparent when the child becomes mobile at around the age of one and has a larger, open environment to explore. The child is drawn towards small objects such as pebbles and tiny insects. This is demonstrated through an example from Montessori (1972) in the Secret of Childhood, when a fifteen month old child sitting on the paving outside suddenly burst into laughter.The adult saw nothing until a small insect was pointed out by the by the child it is as though the child looks for items that are invisible to adults. The period of sensitivity for social aspects of life occurs at around three years of age when the child begins to realise that they are partof a group. The childs social interest is exhibited first as an observing activity, and later develops into a desire for more active contact with others (Lillard, 1972, page 36).Therefore, with providing an environment where a child is free to obs erve, experiment and explore amongst their peers without intervention, apart from when antisocial behaviour is presented, a child naturally forms correct social behaviour and a willingness to help others. The teacher needs a concrete understanding of the sensitive periods to be able to provide a suitable environment for a child to develop. A prepared suitable environment for example includes, clearly ordered, organised and readily available materials, child-sized furniture, low shelves and real life objects.Along with the freedom to explore and move around freely, these aspects all take the needs of the child into consideration, although observation of the childs abilities and interests by the teacher is equally as important for the teacher to make required adjustments to suit the childs needs at certain points in their development and particular sensitivities. It is important to support the requirements of the sensitive periods as once this period passes the child will no long ach ieve his optimum development with this aspect of his inner self with ease.Also if a sensitive period is neglected it may hinder the development with other aspects of the childs development. In Montessoris words, If he passes through a period sensitive to order, the disorder he perceives can be an obstacle to his development and a cause of abnormalities (Montessori, 1972, page 49). Therefore a thorough understanding of a childs periods of growth and sensitive periods as well as exceptional observational skills is essential for a teacher to establish a desired environment to provide the opportunities for the sensitive periods to flourish.Bibliography Lillard, P (1972), Montessori A Modern Approach, Schocken books, New York Montessori, M (1972) The Secret of Childhood, Ballentine, New York Montessori, M (2007) The absorptive Mind, Montessori-Pierson Publishing Company, Amsterdam Standing, E. M (1998) Maria Montessori Her Life and Work, First Plume Printing, America www. casadimir. org Casa di Mir Montessori School (undated). Montessori Philosophy http//www. casadimir. org/montessoriphilosophy. htm

Geographically Weighted Regression to Model Housing Prices

geographically Weighted atavism to Model Housing PricesIntroductionIn chapter 2, HPM has been employ to molding the relationships mingled with characteristics of property and neighbourhood. However, HPM treats the wholly living accommodations market as a single homogenous market and assumes a stationary handle, i.e the parameter estimates be assumed to apply equally e trulyplace space. This presumes the influences of various factors on family line prices in one post be the equivalent as those in another reparation so that space, place and fix do no matter (Foster refer).However, as shown in Chapter 2, the residuals derived using HPM argon correlated. Additionally, Chapter 3 shows that when MLM address is employed to account for spacial heterogeneity, the effects of those various factors in fact deviate across neighbourhoods at different scales and in that location ar great price differentials between neighbourhoods. The global approach, such as HPM, masks those l ocal anesthetic deviations from this second-rate relationship.Disadvantages of MLMAlthough MLM approach takes into account spacial heterogeneity by specifying the spacial units as levels in the representative, there are some weakness of this approach. Firstly, there is no harmony on the definition of neighbourhoods (Kearns and Parkinson 2001 2103), so the specification of the macro level units (i.e. neighbourhoods) is fairly arbitrary. In the past, census boundaries (),administrative boundaries (.), or school catchment areas (goodman) chip in all been used to demarcate the whole syndicate market into smaller submarkets, or local neighbourhood areas. Some researchers combined a series of dataset, such as travel-to-work, immigration and house price information and constructed a so-called housing market areas (HMAs)(..) . HMAs neither match the census boundaries, or the administrative boundaries, hardly instead, they represents.. . The existence of spacial dependence in geogr aphical data means that the observations that are most spatially dependent in the locations that are close to each other should constitute a neighbourhood. A pre peg downd hierarchy of spatial units based on administrate or census boundaries may not necessarily appropriate.Secondly, MLM1 treats space and assumes that homogeneous spatial process applies in spite of appearance the neighbourhoods and discontinues at the boundaries of the neighbourhoods. (). Additionally, the highest level of spatial units (for example, MSOAs in our analysis) are assumed to be spatially dependent. This assumption is unrealistic because the effect of a neighbourhood is to a greater extent likely change gradually from one neighbourhood to its adjacent ones rather than completely stops, the so-called spill-over effects. Therefore, there might be presence of spatial dependance between MSOAs that MLM is unable to capture.In contract, GWR (Brunsdon et al, 1996..) relaxes the assumptions of the effects of v arious shiftings being constant over space (Dark, 2004,Mitchell, 2005andShi etal., 2006) and treats space as continuous. It calibrates locally a spatially variable coefficient reversal model for each location of the claim area by weight the attri moreoveres of it neighbouring locations based on distance-decay functions (.). The attributes of neighbours of a consortted location are all considered so the spatial settlement and heterogeneity buttocks be taken into account in this approach (Paez 2005). This chapter therefore introduced this type of example technique to explore the spatial variations that may exist in the relationships between house price and its predictors.Purpose and Structure of the ChapterThe aim of this chapter is to identify whether the relationships of house prices and a wrap of characteristics of houses and neighbourhood attributes) are relatively stable, or they vary substantially over space? If there are spatial variations, how does the relationships vary within and between neighbourhoods and how does this variation differ from the entrusts derived from MLM approach? In addition, how good is the GWR approach in terms of its predictive capability, equalityd with MLM.?In the next section, a brief description of this technique is introduced. Section 3 follows with a review of previous applications of GWR is presented. The proposed study in relation to the empirical implementation of the technique then follows in section 4. The final section summarise the relation between GWR and MLM the results and discusses the appropriateness of both techniques.4.2 Brief Description on GWR ModelsWhat is GWR?GWR technique is fully descried by Fotheringham etal., 20022 and just a brief description of the approach is presented here. GWR is a spatial analysis technique that takes into account spatial autocorrelations among the observations in surrounding locations by allowing for spatial nonstationarity in the linear regression coefficients for eac h location. In GWR literature, the location can be a point or an aggregated area.describe local geographical variations in the relationships between a response variable and its instructive variables by a set of local estimates for all the predictors for each geographical location (Fotheringham et al. 2002). A set of estimates and bill errors for each local coefficients are green goodsd by focusing each location in the study region and weighted intercellular substance of its nearby observation.The basic GWR equation can be written as (4.1)Where denotes the coordinates of the th point in a two-dimensional study area is the dependent variable at point , is the estimated intercept at point , ( represents the estimated coefficient for variable at point , is the independent variable of the th parameter at location , and is the error term for the local model at point .The theme of ( is derived using weighted least squares (WLS) regressions (Moore and Myers, 2010 Fotheringham et a l., 2002) by weightiness the observations near location in accordance with their distance to that fit point. It is given bywhere is a stroke matrix denoting the geographical weighting of the observations around the fit point .WeightingThe weighting is based on the distance between the regression location and its close neighbours, defined as bandwidth. The points in closer proximity to location is given more weight and therefore has more influence on the estimation of than the observations that are further away to location . A number of weighting schemes are available, but they tend to be Gaussian or Gaussian-like function, which is the types of dependency generally plant in spatial processes (Forthemham). Two Comm only used distance-decay functions in GWR are Gaussian and Bi-square function (Fotheringham et al. 2002), which are expressed as belowGaussian Bi-squareWhere is the th element of the diagonal of the matrix of the geographical weights , is the bandwidth, a threshol d distance that any observations beyond this distance volition not be used for calibrating the local model, and represents the distance between observation and focus point . When and coincide, the weighting equals to 1.Source Gollini et al (2014) GW model an R Package for Exploring Spatial Heterogeneity using Geographically Weighted Models both(prenominal) functions are continuous up until the bandwidth, but the weights of Bi-square function decrease faster than that of Gaussian function and eventually become zero at the boundary of the bandwidth, while the weights of Gaussian function do not become zero. Both of the weighting functions will be tried in the planned research.BandwidthBandwidths can be specified either as fixed or adaptive (in terms of physical distance). The physical distance for adaptive bandwidth is changeable according to the spatial density so as to capture a fixed nearest neighbours for each local model a shorter distance for areas where observations are de nse and long-lasting distance when data are sparse. The benefit of using adaptive bandwidth is that it can ensure sufficient local information be utilised for areas where observations are spatially scares and reduce the estimate stochastic variable for local coefficient and still reveal subtle local variations where observations are dense (Fotheringham et al. 2002). Therefore, adaptive bandwidth will be used in the planned research as the density of house price data vary geographically.The size of bandwidth affects gradient of the kernel and thus the rate of decay function. A small bandwidth have fewer observations included in the local model and rapid decay whereas a large bandwidth will have more observations in the local model and a smoother weighting scheme. The size of the bandwidth is important as if the bandwidth is too small, although the model would fits better for the local observations, but at the same time local noise may in like manner be fitted thus the local estima tes will have large variances. Conversely, if the bandwidth is too large, although the variances will become smaller, but the estimates of local coefficients are based on a much larger area and result in biased estimates which masks the true local relationships, especially if the relationships vary dramatically over small areas. This is the so-called bias-variance trade-off (Fotheringham et al., 2002)3. The effective number can be used to reflect bias-variance trade-off in GWR, which is a round of the number of observations that have been used effectively for calibrating the local model.Bias-Variance Trade-OffTo find the best bias-variance trade-off, an appropriate weighting function and optimal bandwidth need to be selected. It has been argued that the selection of bandwidth selection is far more important than the weighting scheme as the weighting all decreases as distances increase by all weighting functions but the size of bandwidth decides the degree of decay (Fortherham). The optimization process is generally exploratory and can be very compute-intensive process as it requires all the local regressions fitted at each step4. It can be achieved by either cross-validation order or use corrected Akaike information criterion (AICc) (Fotheringham et al. (2002).Leave-one-out cross-validation (LOOCV) is a commonly used cross-validation method in GWR, where for each local model, it is validated by using all the cases except for one observation and the model is tested on that single observation. The bandwidth which produce the smallest root mean square prediction errors for all the dependent variables of all the local models is deemed as the optimal bandwidth. AICc is an indicator of goodness-of-fit and can be used to analyze competing models while taking into account the complexity of a model. A lower AIC score indicate a better fit of a model. As a rule of thumb, a decrease of 3 in AIC of two competing model score indicates an improvement in the model fit for the model with lower AIC (Fotheringham et al 2002 Zhang etal., 2011).It is common though to get different optimal bandwidth from the two methods as the criteria for optimal is different for AICc and for CV5 and the AIC value is not based on prediction of the conditional variable (6..). In addition, AIC score can be corrected for small sample size, while classical CV method tend to produce under-smoothed result for small sample size7. One thing is note is that AIC should be avoided when the sample size is large as it requires the creation of an n by n matrix 8so the optimization can be very slow9. Both method will be tried out in the planned research.Why Use GWR and when?As mentioned earlier, when there is spatial dependency between variables and spatial non-stationarity, GWR can be used to disaggregate global relations to local levels to obtain a better disposition of spatial data in more details. As every local model is fitted to local observations, it fits better to data than a global model and residuals are generally lower and less spatially dependent. The outputs, the estimates of local coefficient are specific to each location.In Chapter 2, Morans I has been used and indicate that there is statistical significant spatial autocorrelation within both house prices and the residuals of HPM results. This means that the global fitted coefficient value of HPM does not represent detailed location variations adequately and GWR should be used in this instance to taken into account the spatial dependency and examine the heterogeneity in housing market.A review of GWR approach in house price estimationThis section reviews the application of GWR technique with a focus on residential real estate, as well as the comparisons of GWR with a range of other methodologies. The section will conclude with the identification of the research gap and thus the contribution of the current chapter.Application in Real Estate military ratingGWR has been applied to a number of field, including land use (Geniaux et al. 2011.), environment (Harris et al. 2010a), health (Comber et al. 2011, Helbich et al. 2012b, Yang and Matthews 2012 10) and crime studies (Leitner and Helbich 2011), economics (11), regional studies (12) and residential real estate studies (Kestens et al. 2006 Bitter et al. 2007). In terms of the application to real estate, GWR has been used to investigate the effects of the locations and surrounding neighbourhood characteristics, such as ,the effects of accessibility, such as the new jalopy transitway in..((Mulley, 2013), infrastructure availability in .(Cellmer, 2012), and the effects of open space amenities (Nilsson, 2014).GWR has also been used to identify housing sub-markets (Borst Mccluskey, 2007 Crespo Grt-Regamey, 2013 Helbich, Brunauer, Hagenauer, Leitner, 2013).GWR compared with other modelling techniquesGWR has also been compared with a few valuation tools in real estate, such as multiple regression analysis (MRA), simultaneous auto regressive model (SAR), Artificial neural networks (ANN), spatial expansion method (SEM) and Spatial lag model (e.g., Brunsdon et al., 199913 LeSage 199914 (Bitter, Mulligan, Dallerba, 2006 Helbich, Brunauer, Vaz, Nijkamp, 2013 McCluskey, McCord, Davis, Haran, McIlhatton, 2013 Yu, Wei, Wu, 2007).More specifically Bitter, Mulligan, Dallerba (2006) demonstrated in their study that GWR was superior to spatial expansion method ( define briefly .)in terms of predictive accuracy and explanatory power when applied to examine the marginal price of key housing attributes in the Tucson, Arizona housing market. McCluskey, McCord, Davis, Haran, McIlhatton (2013) also showed that GWR outperform MRA, ANN and SAR in term of predictive accuracy, transparency, and cost-effectiveness and offered when applied to 2,694 residual properties in for real estate price estimation. In a case study of spatial heterogeneity in Austria, Helbich, Brunauer, Vaz, et al. (2013) extended GWR to a mixed-GWR(MGWR ), which allows some coefficient to be stationary while others to be non-stationary. This approach is more flexible and parsimonious than standard GWR (Wei and Qi, 2012). Both MGWR and GWR has smaller prediction errors in comparison with a global approach, such as OLS, SAR and spatial two stage least square functioning (S2SLS)15.There are other extensions of GWR. To deal with cross-sectional time series data, GTWR (Huang, Wu, Barry, 2010) was developed to integrate both secular and spatial information in the weighting matrices to capture spatial and temporal dependency and heterogeneity16 . GTWR is able to model spatial and temporal nonstationarity simultaneously and therefore offers a better goodness-of-fit. LeSage (2003) incorporate a Bayesian treatment into GWR in order to improve the estimates of GWR parameters. Contextualized Geographically Weighted Regression (CGWR) was developed by adding contextual variables into standard GWR. The research applied this approach to model s patial heterogeneity in the land parcel prices of Beijing in China and demonstrated that the incorporation of contextual information improved the model fit.However, multicollinearity between explanatory variables may result in unstable results in GWR models and cause more problem for GWR than in a global regression model (Lloyd 2007). Therefore, peak caution should be exercised when analysing the spatial patterns of local coefficients derived from GWR (Wheeler Tiefelsdorf, 2005). A range of diagnostic tools was proposed and usage of PCA to identify the most influential predictors or integrating ridge regression into the GWR framework (D. C. Wheeler, 2007) can help stabilize GWR regression coefficients.There is only limited comparison of GWR with MLM, or random coefficient model (RCM). These two approaches are very different in terms of its underlying assumptions of the spatial process and yielded completely different results in the study of long-term illness in the UK (Brunsdon, A itkin, Fotheringham, Charlton, 1999).There has no published research that compares GWR with MLM in terms of their capability to model spatial heterogeneity of house price data and their predictive accuracy. In addition, although GWR can be applied at any geographic scale of measurement, in practice however, may applications and previous research applied it to an coarsely aggregated scale collectible to the availability of data or keep anonymized information. Unlike previous studies, we have geo-code the location of each house based on its unit postcode location, which only contains typically around 15 residential addresses17. We hope to offer further insight into the geographical variation of the relationships at this detailed level, which previously might be disguised in previous research when the level of analysis was carried out at a much coarser scale.Planned ResearchStandard GWR is applied to the same dataset in chapter two and three, the house price data of the Greater Brist ol area. Two extended version of GWR, GTWR and CGWR, will be explored with the former to capture the temporal dependency and heterogeneity and the later to incorporate contextual information into the model. In GWR and CGWR, the whole dataset will be split into yearly data to avoid the potential temporal autocorrelation within the data. There is no need of doing so in GTWR, as the time of sale has been taken into account in the model.Individual house characteristics are all categorical variables as described in Chapter 2 and will be modelled first and then neighbourhood variables will be added in the succeeding models.The planned procedures and a few methodological issues are addressed as follows. Firstly, before carrying out actual modelling of GWR, whether there is significant spatial autocorrelation within the data, which can be between the response variables and its lagged values or between the explanatory variables and their lagged value. Two most commonly used weighting functi on, Gaussian and Bi-squares functions will be used, although it has been shown that the selection of the weighting function does not have as much an effect on the results as the selection of bandwidth (Fotheringham, Brunsdon, and Charlton 1998). If it is the case, just one weighting function will be used in the subsequent yearly models and the focus will be one the optimization of bandwidth. An adaptive bandwidth is proposed, as there is a good mixture of rural/urban of housing stock in Greater Bristol and the density of the house sales varies dramatically over space. Both CV and AIC will be used to obtain optimal bandwidth and measure model fit as it was shown in the past that the two methods resulted in different optimal bandwidth and regression coefficients (18).Once a weighting function and bandwidth has been selected, the weighting matrix can are defined and used to estimate the coefficient for every location based on equation (4.1) and calibrating local GWR. The standardised r esiduals and the parameters, and their estimated standard errors will be mapped to investigate whether they vary spatially19. This will also be compared with the map of the shrinking estimates of the neighbourhoods (OAs, LSOAs and MSOAs) derived by MLM in previous chapters. It is expected that the mapped patterns of MLM coefficient exhibit more noise than that of GWR, since GWR is essentially a spatially smoothing calibration. All of the model caliberation will be conducted in R, using GWmodel package as this software is free and the process can be easily replicated.Lastly, the predictive accuracy of GWR will be measured and compare with MLM. R squared is used for goodness of fit of the model and it measures the proportion of variation in the data that is explained by the model. Adjusted Rsquared takes into account the complexity of the model in terms of the number of variable that are specified in the model. It is expected that extended version of GWR, GTWR and CGWR, may provide b etter model fit and more accurate predictions based on their previous applications.In the past, there has been criticism that GWR cannot produce confidence intervals (..) and the significance of the estimates for parameters cannot be tested. However, Monte Carlo significance tests have been used to test whether there is significant variability (..) so this test is also planned to test if the spatial variation of the coefficients are statistically significant. hazardous bootstrap approach as suggested by by Hardle (1990) and McMillen (2004) can also be used to produce a weighted average of the variance of the separate parameter estimates.ConclusionGWR generally give much better fits to the data and the residuals are less autocorrelated. Its advantages over MLM is that it no longer treats space as discrete, which more likely resemble the spatial process in reality, and it models both spatial dependency and heterogeneity. In addition, it is essentially a non-parametric approach that d oes not requiring any assumptions with respect to the predictors, which can be categorical or the underlying distributions of the predictors can be highly skewed. There is no need to specify a useable form to produce the estimates of spatially varying parameters (Brunsdon et al 1998). The underlining concept of letting the data speak for themselves make it a good exploratory tool 20 for spatial analysis. This concept is very much similar to another modelling technique, ANN, except that in ANN, there is no implication of nearer locations have more influences on the estimates of local coefficients than locations that are further away as in GWR. This although unlikely in reality, but it might happen. How does GWR compared with ANN will be discussed in the next chapter. striking GWR and ANN a set of estimates of spatially varying parameters WITHOUT specifying a functional form let the data speak for themselves (Chris et al 1998)1 the parameter estimates are assumed to be randomly dist ributed with either a finite (Wedel and Kamakura 2000) or a continuous mixture distribution (Aitkin 1996).2 And Legendre, 19933 Check Bias-variance trade-off MLM (Goldstein 1987) and Ridge Regeression (Hoerl and Kennard 1970a, 1970b)4 check reference Schabenberger and Gotway (2005 316-317) statistical methods for spatial data analysisWaller and Gotway (2004, p434) applied spatial statisticsand Lloyd (2007 pp 79-86) local models for spatial analysis5 http//webhelp.esri.com/arcgisdesktop/9.3/body.cfm?tocVisable=1ID=-1TopicName=Interpreting GWR results6 Housing Sub-markets and Hedonic Price Analysis A Bayesian Approach byDavid C. Wheeler1*, Antonio Pez2*,Lance A. Waller1 and Jamie Spinney3Chapter 4 7 Encyclopedia of Geographic Information Scienceedited by Karen Kemp (p183)8 (gwr.sel spgwr)9 NOTE AIC be applied in non-Gaussian GWR( Local Models for Spatial Analysis, Second Edition By Christopher D. Lloyd) 10 Modelling spatially varying impacts of socioeconomic predictors on mortality o utcomes, J Geograph Syst (2003) 5161184, DOI 10.1007/s10109-003-0099-7, proposed for modelling spatially varying, predictor effects on a disease or mortality count outcome The methodology is illustrated by suicide mortality in 32 London Boroughs over the period 19791993, in terms of area deprivation and a measure of social fragmentation disease mapping methods11 spatial HETEROGENEITY AND THE WAGE CURVE REVISITED*Simonetta Longhi, ISER, Peter Nijkamp12 The Geographic Diversity of U.S. Nonmetropolitan Growth Dynamics A Geographically Weighted Regression Approach Mark D. Partridgey Dan 5. Rickman, Kamar AU, and M, move Olfertte.st for geographic heterogeneity in ihe growth parameters ami compare iliem to global regression estimates. The results indicate significant heterogeneity in the regression coejjkients across the country, most notably for amenities and college graduate shares. V.sing GWR also exposes .signiftimt local variations that are masked by global estimates13 A Compariso n of Random-Coefficient modelling and Modeling and Geographically Weighted Regression for Spatial Non-Stationary Regression Problems, Geographical and Environmental Modeling, 3 (1), 47621

Geostatistics and Advance Reservoir Modelling Essay

Geostatistics and Advance Reservoir Modelling Essay run into 1. All moulds are based on compares (next page) and plotted manual(a)ly in excel to show their respective appearance. Spherical model fails to proceeds as lag exceed the practical range.Modelling and interpret VariogramJahanzeb Ahsan (8529193) B.Eng Petroleum EngineeringIntroductionthither are a dozen different variogram models. Four most frequent used areSpherical glitter behavior at origin and more linear.Exponential Greater slope than spherical i.e. relates to more random variables than spherical model.Gaussian Using only Gaussian in absence of nugget abide lead to problems in Kriging.Power Also associated with fractal models.For above equations h shows lag distance, a is the practical range and c is sill.There are three basic terms in all variograms sill, nugget and range, as shown in figure 1. Sill is the value obtained after stabilizing the experimental curve by fitting a variogram model. It signifies zero or n o correlation of our spatial data, since variogram send word be imagined as an inverse of variance graph, the sill shows the maximum variance i.e. in that respect is no distance (zero lag) between the data, hence maximum correlation (for variance). Range is said to be the maximum distance for which correlation between 2 points can exist beyond this autocorrelation cease to working In terms of geology vertical range is greater than horizontal range due to difference in scale, nonrepresentational anisotropy. When comparing a horizontal variogram with vertical having different sills but same range one may conclude it as a zonal anisotropy, a lot due to stratification and layering. Range can also varies with type of model used. Based on the model equation figure 1 shows a manual attempt to demonstrate Gaussian model reaches sill (range is at lag 14) before exponential model having greater range (at lag 18). Range is said to be directionally dependent if anisotropy exist geology is more anisotropic vertically than laterally. Nugget is an unavoidable error at origin in data. No correlation in data can lead to Pure Nugget, an ideal model have zero-nugget. For ease of understanding one can term it as an inherited error e.g. from measuring instruments. Figure 1 shows a nugget of 1.7732E-06.Noise can appear due to lose of data pairs and is more prominent in directional variogram, hence they do not show overwhelming evidence of anisotropy. Pure nugget models are also know as white noise i.e. the data shows no spatial correlation.Interpreting experimental Variogram (figure 1)For variogram or any other geostatistical method precision and optimality increases when the data is stationary and normally distributed i.e. mean and variance does not vary considerably. High deviation from data normality and stationary can result in complications.A skewed histogram influence the durability in estimation of variogram. Similarly if theoretical sill (see figure 1) is downsta irs experimental variogram a trend in data exist, which should be removed before interpreting the experimental variogram, however this does not mean it will bring the problem no trend in dataset, see figure 2 (b). In other words geological data like porosity, grain size of it and permeability often shows trend which result in negative correlation as distance increases resulting in variogram to exceed sill (not in this case).Cyclicity (geological cyclicity) also known as hole-effect is another important phenomenon variograms exhibit (purple line, see figure 1). Periodic repeated variations like facies and other physical properties yield a cyclic behavior on variogram and like in figure 1 cause the variogram to deviate (below sill in this case). Cyclicity often diminishes over increasing distance as these cyclic repeated geological variations are not consistent. This hole-effect phenomenon maybe insignificant in terms of overall variance but nonetheless should be included in a var iograms interpretation.Table 1 shows the skewness as negative, however not perfectly skewed, however one can assume it due to lack of data since our range of measurement is only 39 f.t. Table 1 concludes our data is not perfectly normally distributed, hence our variogram model and Kriging will be affected significantly.Mean0.079937Median0.0805Mode0.0813Standard Deviation0.003662Sample Variance1.34E-05Skewness-0.657Range0.0151Minimum0.0709Maximum0.086Sum6.315Count79Kurtosis-0.1163Depth Length of data (MD) f.t.39Table 1. Basic statistical analysis of dataConclusionA real variogram consist of all or combination of features such as hole-effect, sill, range, an experimental data set fitted with appropriate model.Variograms such as rodograms or modograms or relative pairwise variograms are used when simplex variograms fail to detect anisotropy and range.Amount if data is a big constraint in variogram modeling such that bigger the data, more accurate model.Spherical model fail to fit when lag distance exceed the practical range (like in our case).Lack of appropriate software and manual input of model equation in excel shows an approximate guide to how spherical, exponential and Gaussian model will behave. Gaussian model gives the best fit model and least nugget effect.A trend and sparseness in data greatly degrades the authenticity of variogram.Often biased especially when modelled inaccurately.Despite having its disadvantages a variogram can be a effectual tool in heterogeneity analysis an indicator variogram which converts the values into 1s and 0s is notably useful in quantification of lithological and geological units and future predictions. Kriging interpolation technique uses variogram.ReferencesBohling, G., 2007. In opening TO GEOSTATISTICS. Boise Boise State University, pp. 15-25.Dubrule, O., 1998. Geostatistics in Petroleum Geology. Tulsa American Association of Petroleum Geologists.Emmanuel Gringarten, C. V. D., 2001. Teachers Aide Variogram Interpretati on and Modeling. Mathematical Geology, 33(4), pp. 507-534.Fanchi, J. R., 2006. Principles of Reservoir Simulation. third ed. Oxford Elsevier B.V.Gregoire Mariethoz, J. C., 2014. Multiple-point geostatistics. 1st ed. s.l. John Wiley Sons, Ltd.Hodgetts, D. D., 2014. Geostatistics and Stochastic Reservoir Modelling. Manchester s.n.Huihui Zhang, Y. L. R. E. L. Y. H. W. C. H. D. M. G. C., 2009. Analysis of variograms with various sample sizes from a multispectral image. Int J Agric Biol Eng , 2(4), pp. 62-69.M. J. Pyrcz, C. V. D., n.d. The Whole Story on the Hole Effect. Online Available at http//ceadserv1.nku.edu/longa//mscc/boyce/gaa_pyrcz_deutsch.pdf Accessed 9 November 2014.

Advanced Glycation End Products (AGE) for Diabetes

Advanced Glycation End Products (AGE) for DiabetesDiabetes mellitus is a syndrome which defined as a group of metabolic diseases characterized by hyperglycemia, that result from skimpy production of insulin, or frame cells poorly respond to the insulin that is produced, or some(prenominal). Insulin is a hormone produced in the pancreas and secreted in the seam to maintain blood glucose in the body with and through enables body cells to absorb glucose, to turn into energy. If the body cells enable to absorb the glucose, the glucose will accumulates in the blood (hyperglycemia), leading to many and different potential checkup complications (Harmel Mathur, 2004).Diabetes project some(prenominal)(prenominal) categories but the majority of cases fall into two categories which argon reference 1 diabetes mellitus and emblem 2 diabetes mellitus. These two events are powerful and passing independent risk elements causing coronary arterial blood vas disease, stroke, peripheral a rterial disease and organ legal injury and dysfunction including eyes and nerves (Harmel Mathur, 2004).Type 1 Diabetes MellitusType 1 diabetes mellitus resulting from pancreas failure to produce insulin hormone. Person at risk of developing type 1 can be identified by doing serologic test markers that showing evidence of auto tolerant destruction of beta cells (islet cells) of the pancreas which is responsible for insulin production. Type 1 diabetes is manifested in childhood and early adulthood, but can affected role present at any age (Goroll Mulley, 2009).Type 2 diabetes mellitusPatients with type 2 diabetes mellitus are prone with wide range of series complications. Type 2 is characterized by amply blood glucose due to insulin resistance and relative insulin deficiency. There are 20.8 million people in United State with type 2 diabetes mellitus. Type 2 diabetes traditionally is seen in elderly people. However it is diagnosed in obese children. Many studies shows that type 2 diabetes mellitus are associated with high calorie diet, corporeal inactivity and life style (Feinglos Bethel, 2008).Other Types of DiabetesThere are other types of diabetes but they are less common but endurings who are underlying daub or disease process can be identified in a relatively specific manner. These types are Genetic defects of beta-cell function, Diseases of the exocrine pancreas e.g Fibrocalculous pancreatopathy, Endocrinopathies and cystic fibrosis and uncommon forms of immune- meddled diabetes.Diabetes mellitus complicationsDiabetic complications can be grouped into macrovascular and microvascular disease. Macrovascular diseases are result from atherosclerosis which develops in earlier age in tolerant with diabetes. There are several factors contribute to atherosclerosis such as lipoidemia, hypertension, increased platelets adhesion and aggregation, elevated factor V, factor VII and fibrinogen concentration. Macrovascular diseases are seen in both type one and two of diabetes mellitus and they entangle coronary heart disease, Ischemic stroke and peripheral vascular disease (which can lead to ulcers, gangrene and amputation) (Winter Signorino, 2002).Whereas, Microvascular complications is seen in type one diabetes mellitus. Hyperglycemia damages the basement membrane of capillaries in the retina and glomerulars which leads to retinopathy and neuropathy. Microvascular diseases involve neuropathy (nerve damage), nephropathy (kidney disease) and vision disorders (eg retinopathy, glaucoma, cataract and corneal disease). pull aheadmore there are other complications of diabetes include infections, metabolic difficulties, dental disease, autonomic neuropathy and pregnancy problems (Winter Signorino, 2002).Several clinical research show a strong relationship between hyperglacemia and diabetic microvascular complications in both type 1 and type 2 diabetes. High glucose and insulin resistance admit important roles in the patho genesis of macrovascular complications due to atherosclerosis. Diabetes-specific microvascular disease in the retina, glomerulus and vasa nervorum has same pathophysiological features. Intracellular hyperglycaemia causes abnormalities in blood flow and increased vascular perme force which leads to decreased activity of vasodilators such as nitric oxide, increased activity of vasoconstrictors such as angiotensin II and endothelin-1, and elaboration of permeability factors such as vascular endothelial growth factor (VEGF) (Brownlee, 2001).There are several factors which contribute the ecesis of inflammation, atherosclerosis and diabetes mellitus complication. These factors are hyperglycemia, accumulation of move glycation endproducts, dyslipidemia and aerophilic stress which lead to endothelial dysfunction resulting in thrombotic complications and cardiovascular (Altman, 2003).In type 1 diabetes mellitus, high blood glucose is usually as result of low train of insulin secretion whereas in type2 hyperglycemia is caused by resistance of insulin at the cellular level. Low insulin levels or insulin resistance enable the body to convert glucose into glycogen (a starch-as source of energy which stored in the liver).In type 1 diabetes, pancreatic beta cells are attacked by auto-immune which cause infiltration of inflammatory cells and increased way and secretion of S100-calcium binding protein and high-mobility group protein 1 (HMGP1) through inflammatory cells which consequently lead to islet damage and increased blood glucose level, subsequent accumulation of AGE (Bierhaus Nawroth, 2009).Comparing to T1D, in type 2 diabetes mellitus, different metabolic disturbances stimulates inflammatory cells to secretes behave ligands, which cause low-grade inflammation and increased oxidative and carbonly stress, all these play roles in promoting AGE validation and RAGE flavour in respective organs (Bierhaus Nawroth, 2009).Insulin resistance and excitationIn diabetic patients with hyperglycemia, cause by 1) despoiled insulin secretion by the pancreatic -cells, 2) vigour insulin resistance, and 3) hepatic insulin resistance all play central roles in the development and progression of glucose intolerance.As I mention before, type 2 diabetes mellitus (T2DM) is characterized by insulin resistance in liver and vigour and impaired insulin secretion. Also include deranged adipocyte metabolism and modified fat topography in the pathogenesis of glucose intolerance in T2DM. Fat cells are resistant to insulins antilipolytic issue, which result in increased plasma expel fat dosage levels. Long period of increasing plasma fat fee acid stimulates gluconeogenesis, advertise hepatic and muscle insulin resistance, and impairs insulin secretion in genetically predisposed patients. These passages of FFA- bring forth are known as lipotoxicity. Fat cells Dysfunction makes excessive amounts of insulin resistance-inducing, inflammatory, and atherosclerotic-provoking c ytokines and fails to secrete normal level of insulin-sensitizing adipocytokines. Enlarged fat cells are insulin resistant and have minimized capacity to store fat. When storage capacity of adipocyte is exceeded, lipid overflows into muscle, liver, and perhaps -cells, resulting in muscle, hepatic insulin resistance and impaired insulin secretion.In type 2 diabetics, the ability of insulin to stop lipolysis and to decrease the plasma FFA levels is markedly impaired. It is clearly that chronic elevated levels of plasma free fat acid leads to insulin resistance in muscle and liver, and impair insulin secretion. In addition to FFA in plasma, increase stores of triglycerides in muscle and liver which correlate closely with the presence of insulin resistance in winds. The triglycerides in liver and muscle are in a state of constant turnover, and the metabolites of intracellular triglyceride lipolysis impair action of insulin in liver and muscle (Bays et al, 2004).This pathway of events has been referred to as lipotoxicity . The accumulation of lipid in dipocytes leads to activate NADPH oxidase which increases the production of excited atomic number 8 species (ROS). The mechanisms increased production of cytokines including TNF-, IL-6 and monocyte chemoattractant protein-1 and reduce the production of adiponectin (Shoelson et al, 2006).Furthermore accumulation of lipid activates the protein response to increased ER stess in fat and liver. All these have accumulated to energise lipotoxicity as an important cause of -cell dysfunction (Bays et al, 2004).Receptor for advanced glycation end products (RAGE)RAGE is type I transmembrane receptor of the immunogloblin superfamily. It is a receptor for advanced glycation endproducts (AGE). It is about 45-KD a protein and it is consist of 403 amino acids in human, appreciate and mouse. Its extracellular region consists of one V-type (variable) immune serum globulin domina, which followed by two C-type (constant) immunoglo bulin dominas (Basta, 2004).Its name drive from its ability to bind AGE and also it is known as AGER. It is located within the major histocompatibility difficult (MHC) crystallize III region on chromosome 6, which contains numbers of genes convolute predominantly in inflammatory and immune responses and several components of complements. Beside AGE, RAGE is able to bind several ligands therefore is called a pattern-recognition receptor. These ligands are high-mobility group protein 1 (HMGP1), S100-calicum binding protein (S100b) which is family of pro-inflammatory cytokines and it can be found in any inflammatory lesion and vascular walls of diabetics patients, amyloid--protein and macrophage-1 glycoprotein (CD11B/CD18). RAGE plays role in diabetes and other metabolic disease. The interaction of RAGE and its ligands cause pro-inflammatory activating. It is involved in several diseases such as innate immune response, mediating immune and inflammatory response, cancer promoting an d progressing and microvascular and microvascular diseases (Bierhaus Nawroth, 2009).Moreover, RAGE expression is increased in the cells of the vascular walls, at the site where AGEs and S100/calgranulins are accumulated, including endothelium vascular smooth muscle cells, glomerular mesangial cells and mononuclear phagocytes. This destrbution of RAGE and its ligands leads to increased cellular activation, causing further raised expression of the receptor (Basta et al, 2004).Recently the studies have been proved that RAGE and its ligands accumulate in diabetes and contribute to its pathology. Serum levels of S100A8/9 and S100A12 increased in type 2 diabetic patients. Also, evidence was provided by raised serum levels HMGB1 were united to coronary artery disease in type 2 diabetes. In addition the studies which have been done in streptozotocin- take a shitd diabetic rats proved that diabetes increased amyloid-beta-peptide (1-40) levels in the brain. Furthermore, CML-modification of S100A8 and S100A9 are seen in inflammatory bowel disease and promote RAGE-mediated sustained inflammatory. Moreover, Carboxylated N-glycans on RAGE eases binding of HMGB and mediates ligation of S100A8/A9 to subpopulation of RAGE on colon cancer cells (Bierhaus Nawroth, 2009).Advanced Glycation End products (AGE) and its biochemical mechanism productionThere are various pathways involved in stimulation of atherosclerosis in diabetes mellitus. However the most important one is formation and deposition of AGEs through nonenzymetic reaction between extracellular protein and glucose and it accumulates within cells of the vascular walls, in the extracellular space, kidney, nerves and retina (Basta et al, 2004).Possible mechanism of AGEs formation arise from intracellular auto-oxidation of glucose to glyoxal, decompounding of the Amadori product (glucose-derived 1-amino 1-deoxyfructose lysine adducts) to 3-deoxyglucosone and fragmentation of glyceraldehyde-3-phosphate and dihydroxyaceton e phosphate to methylglyoxal. These reactive intracellular dicarbonyls (glyoxal, methylglyoxal and 3-deoxyglucosone) which can react with amino groups of intracellular and extracellular proteins to form AGEs (pyrraline, pentosidine, CML, crossline) (Balasubramanyam et al, 2002).Several experimental studies evidenced that advanced glycation end products can alter vascular wall homeostasis in atherogenic through different ways which areAGEs and mononuclear phagocytes InteractionThe binding of AGEs with mononuclear phagocytes mononuclear phagocyte system induces the activation of platelet-derived growth factor, insulin-like growth factor-1, and pro-inflammatory cytokines, such as IL-1 and TNF-. Furthermore the interaction of AGEs with its receptor (RAGE) in the mononuclear phagocytes promotes cell migration (chemotaxis) (Basta et al, 2004).AGEs and vascular smooth muscle cells InteractionInteraction of AGE with smooth muscle cells (SMCs) exhibits the proliferative activity and product ion of fibronectin. SMC growth are in setly mediate by cytokines or growth factors which induced by AGEs in the MPs. Transforming growth factor- (TGF-) act as an intermediate factor in AGE-induced fibronectin formation by SMC(Basta et al, 2004).AGEs with vascular endothelium Interactions alterations of vascular permeability and of adhesive propertiesAs result its unique position and numerous properties, the vascular endothelium has important role in the regulation of extracellular permeability, the maintenance of blood fluidity, metabolism of hormones and vasoactive mediators and the regulation of vascular growth and tone. The endothelium is exposed to AGEs located on go around proteins and cells (such as, diabetic RBCs), also those found in the underlying subendothelial matrix. Receptors for AGEs present on the endothelial cell surface, and mediate both the uptake and AGEs transcytosis, and the internal signal transduction. AGE-RAGE interaction leads to alteration of barrier functi on and an increased permeability of endothelial cells interact with AGEs and increased migrate of macromolecules through the endothelial monolayer. The increase in permeability is associated by alterations of the physical integrity of the endothelium, as shown by the destruction of structures and alterations of cellular morphology (Basta et al, 2004).Also, it has been proved that AGEs cause alterations of endothelial anti-hemostatic functions in vitro, through a reduction of thrombomodulin expression and accompanied with induction of interweave factor expression. The promoting of tissue factor and the decreasing in thrombomodulin activity tilt the dynamic endothelial features with regard to hemostasis from those of an anticoagulant to those of a procoagulant surface (Basta et al, 2004).Intraction of AGEs with endothelial RAGE also causes the depletion of cellular antioxidant defense mechanisms (such as glutathione, vitamin C) and the generation of reactive oxygen species. As a res ult of the increased cellular oxidative stress, NF-B activation occurs, thus inducing the expression of NF-B-regulated genes including, in addition to the procoagulant tissue factor, adhesion molecules, such as E-selectin, intercellular adhesion molecule-1 (ICAM-1) and vascular adhesion molecule-1 (VCAM-1) this past may prime diabetic vasculature towards enhanced interaction with circulating monocytes. Furthermore, the incubation of endothelial cells with EN-RAGE or S100B results in VCAM-1 induction, in a RAGE-dependent manner, as sustained by the inhibitory effect of anti-RAGE IgG or soluble RAGE (Basta et al, 2004).Alterations of endothelium-dependent vasodilatationAGEs linked to the vascular matrix may quench bioavailability of nitric oxide (NO), which is an important regulator of vascular tone inducing smooth muscle cell relaxation. Studies provided that, AGE inhibits NO activity, when it added to NO in vitro. Studies on animal exterminate induced diabetes show that an alterati on of endothelium-dependent dilatation occurs in short period, within 2 months, from diabetes induction. A direct reaction between NO radical and other free radicals which are formed during the reactions of AGEs assumable leads to inactivation of NO. In parallel, advanced glyaction end products promote the expression of the potent vasoconstrictor endothelin-1 changing endothelial function towards vasoconstriction (Basta et al, 2004).These four mechanisms promote and alter vascular wall homeostasis. When mononuclear phagocytes migrate to the site of immobilized AGEs in the tissue, their migrations cease them to interact with AGE -modified surface and become activated. This mechanism cause attracting and retaining MPs in tissue where AGEs deposit. Migration and activation of MPs and T cells (inflammatory cells) promote and cause chronic vascular inflammation through alter vessel wall (Basta et al, 2004).Promotion of atherogenesis pathways by AGEsAGEs are most important factors in end othelial dysfunction in diabetic patients through binding its receptor (RAGE). Advanced Glycation Endproducts stimulate the expression of proinflammatory cells and molecules (Altman, 2003).AGEs can be seriously deleterious to the function of blood vessel walls in various ways. First it causes blood vessel dysfunction through cross bridges among vessel macromolecules. Secondly of damage is that accumulation of AGEs lead circulating blood cells to adhere to the vascular walls which promote atherosclerosis. Third way is through binding different receptors which have been recognized on various cell types such as macrophages, endothelial cells, and smooth muscle cells, renal and neuronal cells (Basta et al, 2004).Hyperglycaemia Mechanisms-caused damageHyperglycemia diverse microvascular and macrovascular complications in diabetic patients through several mechanisms such as increased formation and accumlation of advanced glycation end-product (AGE) activation of protein kinase C (PKC) iso forms and increased generation of reactive oxygen species (Bonke et al,2008)Increased production advanced glycation end-productsAGE promote atherogenesis pathway through accumulation of AGEs and AGEs ligation to RAGE on endothelial cells and macrophages induce inflammation through activating pro-inflammatory cytokines including TNF-, 1L-6 a L-1, induction of reactive oxygen species (ROS) and through increased oxidative stress which lead to degradation of IKBs (IKBs is normally bound to NF-KB to prevent translocation of NF-KB to nucleus). ROS activates NF-KB which results in translocation of NF-KB to nucleus. Activation of NF-KB cause pathological change of gene expression which is highly related to inflammation, immunity and atherosclerosis, increased expression of inflammatory mediators which lead to insulin resistance and increased RAGE expression as well (Basta et al, 2004).Mechanisms by which intracellular production of advanced glycation end-product (AGE) precursors damages vas cular cells. Cellular functions are altered by dicarbonyl advanced glycation end products. Alteration of extracellular matrix proteins results in abnormal interactions with other matrix proteins and with integrins. Alteration of plasma proteins by AGE precursors generate ligands that bind to AGE receptors, inducing conversion of gene expression in endothelial cells, mesangial cells and macrophages.Activation of protein kinase CThe protein kinace C (PKC) family compromises at least eleven isoforms, nine are induced by the lipid second messenger diacylglycerol (DAG). modify DAG-PKC pathway play an important role in diabetic complications. Intracellular hyperglycaemia raises the amount of DAG which activate PKC in cultured vascular cells and in the retina and renal glomeruli of diabetic animals. Hyperglycaemia may also activate PKC isoforms indirectly by AGEs bind receptors and increased activity of the polyol pathway, possibly through increased reactive oxygen species (ROS). AGE stim ulated diacylglycerol (DAG) and activate protein kinase C (PKC) in VSMC. PKC is one of important signal transduction elements involved with multiple cell response. In early studies of diabetes, retinene and renal blood flow abnormalities are due to activation of PKC- isoforms perhaps through depressing nitric oxide production or increasing endothelin-1 activity. Abnormal activation of protein kinase C has several pathogenic consequences It leads to decreased production of nitric oxide in smooth muscle cells and glomerular mesangial cells that is induced by hyperglycemia (Brownlee, 2001).Activation of PKC causes suppression of insulin-stimulated expression of mRNA for endothelial nitric oxide synthase (eNOS) in cultured endothelial cells. Hyperglycaemia activates PKC to increased endothelin-1, permeability of endothelia cells and increased expression of the vascular permeability angiogenesis factor (VEGF) in smooth muscle cells (Brownlee, 2001).Increased generation of reactive oxy gen speciesIncreased generation of reactive oxygen species (ROS) is another possible pathways of diabetes complications especially nephropathy complication. Production of Reactive oxygen species can be result from the activation of various enzymes, including NADPH oxidase, nitric oxide (NO) synthase, and myeloperoxidase, with arising evidence that NADPH oxidase is the major cytosolic source of ROS generation in diabetes. NADPH has homologues that are present within the kidney, namely nox-3, seen in fetal kidney, and nox-4, which is predominately expressed in the renal cortex. AGE bind RAGE induces signal transduction and activate NADPH oxidase in endothelial cells. The interaction between AGE-RAGE enhances production of the cytokine vascular endothelial growth factor (VEGF), which is directly induced by NADPH oxidase and is associated with the pathogenesis of albuminuria in diabetes (Bonke et al, 2008).Diabetes mellitus control and treatment some(prenominal) type 1 and 2 diabetes me llitus are characterized by elevated blood glucose level due to insufficiency of insulin level. Therefore, diabetes patients need to reduce blood glucose level through healthy diet, drugs such as (hypoglycemic tablets and anti-atherosclerosis drugs), insulin injection and regular physical cipher.Healthy DietIn general, healthy diet for diabetes patients should include limit fats (especially saturated fats and trans-fat acid), proteins and cholesterol. Also, patient should consume a lot of fiber and vegetables. Patients with insulin-producing or insulin synthesis treatment should monitor their blood glucose level to avoid hyperglycemia. For example, adults and teenager patients should maintain their blood glucose level between 80-120 mg/dl, and 100-200 mg/dl for children under 12 form old. Type 1 diabetes patients should test their blood glucose level four to more per day. However, in type 2 diabetes, patients recommend to measure blood glucose level 1 to 2 times daily, because glu cose level in type 2 diabetes is more durable than in type 1. Such important tests are important during diet plans and treatment. For example, glycosylated hemoglobin test (HbA1 c) is an indicator for carbohydrate level. Micro and macroalbuminuria indicates of the proteinuria and renal function, for pattern if the teat showed high level of proteinuria then the patient need to lowering protein intake (Flaws et al, 2002).ExercisePhysical exercise is important to manage diabetes complications. unbroken exercise improves the status of both type 1 and 2 diabetes through transporting sugar to muscles, improving blood circulation, and increasing insulin receptors. Exercise has specific cause on diabetes patients include reducing blood glucose level during and after exercise, increased insulin sensitivity, reduced triglyceride level and increased good cholesterol (HDL) (Flaws et al, 2002).However, diabetes patients may have several risks associated with exercise which include hypoglycem ia if patient under treatment with hyperglycemia agents, hyperglycemia and ketosis in insulin-deficient patients, and exacerbation of cardiovascular disease. Therefore, patients who are over 30 year old should be examined before doing physical exercise. These examinations include cardiovascular exams such as blood pressure, blood lipid and ECG, and neurological evaluation such as eye exam (Flaws et al, 2002).Anti-inflammatory drugsVarious drugs in recent clinical practice have been used as anti-inflammatory agents such as thiazolidinedione (TZD) class of PPAR agonists and members of statin class of HMG CoA reductase inhibitors. Both of them have importane anti-inflammatory properities and both have action on glucose homeostasis and cholesterol redusing (Shoelson et al, 2006).TZDs are used to induced insulin sensivity and decresed hyperglicemia in patient with type 2 diabetes mellitus. TZDs drugs inculde pioglitazone, rosiglitazone and troglitazone. The role of TZDs is through bindin g and activiting PPAR to induce a number of gene expression products in a dipocyte. TZDs action are attributed to fat acid in the a dipose tissue. TZD decreases circulating free faty acid and keep faty acid out of musle and liver because accumalte of fatty acide in these tissue lead to insulin resistance. In addition, PPAR is not only present in dipocyte but also in it is expressed in macrophages and other immune cells response, hepatocyte, endothelial cells and vascular smooth mucsle cells (VSMC). TZD play role in reduce the expression of targets gene for cytokines, growth factors, proliferation and migration of cells, and cell cycle progression (Shoelson et al, 2006).RosigliatozoneRosigliatozone decreses the inflammatory markers such as serum C-reactive protein, metalloproteinase-9, white blood cell, tumer necrosis factor- and serum amyloid-A in type two diabetes mellitus (Altman, 2003).Group of studies done on Diabetes RodentsThe recent research has demonstrated that rosiglitizo ne, a PPAR agonist, attenuates diabetes associated atherosclorosis. The reaserch has studied the direct antiathersclosis effects of PPAR afer long beriod of therapy in an experiment of insulin deficiency. This study show that rosiglitezone has no effect on glucose level in both control or diabetes mice. However, rosiglitizone , PPAR (acting as insulin sinstiser) significally decresed plasma insulin level in control mice (Calkin et al, 2005).In addition to Calkin study and his collegous, another study shows that rosiglitazone increase the insulin content and iselt cells number and total mass of pancreas in diabetes rats. However, it is unknown if this is adirect effect of rsiglitazone by activation of PPAR mediated pathway in islets or indirect effect of normalizing the hyperglicemia (Sanchez et al, 2002).Increaesd secration rate of proinsulin relative to insulin is one oa acommon feature in type 2 diabetes mellitus. The normal range of proinsulin in normal subjects is about 2 to 4% of the total amount of insulin and in type 2 diabetes proinsulin rate is about 15%. Curently the studies has been demonstrated that rosiglitazone in type 2 diabetes decrease proinsulin secretion. Proinsulin has been associated with activity of carboxypeptidase E. Fricker and collegous have evidenced that carboxypeptidase are present in secretory pathway and participate in peptide processing. CarboxypeptidaseB has similler feature as carboxypeptidaseE but the precursor sequence of carboxypeptidaseB has been seen in an adult mouse islet cells. Thus suggest that rosiglitazone may has primery effect on increasing the expression of carboxypeptidaseB precursor protein resulting in increse proinsulin to insulin conversion (Sanchez et al, 2002).Further more, rosiglitizone did not affect fasting insluin level in diabetic mice due to streptozotocin which induce insulin insufficency. Also, another group of study has curently demonstrated that short period of treatment with rosiglitizone decrea sed brass instrument area in diabetic mice but had no intention if effects seen were linked to insulin sensitization (Calkin et al, 2005).More current sutdies have evedanced that rosiglitezone treatment in diabetes mice reduce gene expression of the NF-KB subunit p65. Further study has shown that rosiglitizone has no direct effect o glucose-induce upregulation of oxidative stress. Same study had investigated RAGE expression acording to preveious finding that first of all, RAGE expression increse oxidative stress and secondly rosiglitezone which used in culture media alter RAGE expression. The study had shown, the expression of RAGE gene wasnt altered by rosiglitizone (Calkin et al, 2005).Some study showed no world-shaking alteretion in gene expression after TZDs treatment. This is because of almost studies were not preformed in human adipose tissue in vivo but they used adipose tissue of rodents and adipocyte cell line. Actually under such study we should consider about some fac ts that, for example rosiglitazone dose which used in rodents study (10 mg/kg) is higher(prenominal) than human dose about 100-fold. furthermore, TZD may has an action on gene expressed in rodents but it is not necessarily to has same action on human gene. For example carbonic anhydrase 3 protein raised 2-fold after treated the mice with rosiglitazone, however there was no increasing in carbonic anhydrase 3 mRNA in human adipocyte. Although there were similarities in the action of TZD-induced insulin sensitivity, there may be TZD action has different mechanisms on experimental models and human (Kolak et al,2007).Therfore, Im going to discuss about the studies which have been done on human adipose tissue and there findings.Group of studies done in human patient with type 2 diabetesOveractivity of ubiquitin-proteasome system is associated with the inflammation and atherosclerotic plaques in type 2 diabetes. Study by Marfella and his colegous on human diabetes patients has been shown that the PPAR- agonist rosiglitazone reduce ubiquitin-proteasome activity and thuss prevent plaque progression to unstable phenotype in diabetes individulas (Marfella et al, 2006).Also, same study shows, the production of O2- by monocytes is reduced after rosiglitazone treatment. Thus proteasome reduction is induced by inhibition of oxidative stress and polyubiquitination. As oxidative stress induce insulin resistence through NF-kB activation, rosiglitazone enhance insulin sensivity and plaque stability in diabetes patien through reducing oxidative stress and ubiquitin-proteasome activity.Further possibility that, NF-KB activation is inhibited by rosiglitazone through a PPAR- independent pathway. PPAR- agonist inhibits NF-KB translocation and subsequent DNA binding through inhibits immune response which induce degradation of IKBs (Marfella et al, 2006).Rosiglitazone have significant effects on metabolism of faty acid and lowering circuliting non-esterified fatty acid. Therfore rosig litazone prevent islet cells through reducing fatty acid exposure (Sanchez et al, 2002)Kolak and his colleagues investigated the gene of expression in human adipose tissue in vivo in type two diabetes mellitus, befor and after tratment with rosigliatozone and metformin. Reosigliatozone modulates expression of gene which involved in free faty acid synthesis and storage, protein strcture, inflammatory cells inculde macrophage and gene associated in glucose transport and insulin sensivity. Whereas metformin has no effect on these gene (Kolak et al, 2007).TZD decrease the expression of RAGE endothelial protien.From preveious finding, TNF- increase the expression of RAGE on endothelial cells, so

Nature and Logic Essay -- essays research papers

Philosophy 103 Introduction to logical systemThe Nature of Logic Abstract Some of the uses of logic be illustrated, and deductive arguments are briefly distinguished from inductive arguments. I. Logic is the study of the methods and principles used in distinguishing constitute from incorrect reasoning. B. Logic differs from psychology in being a normative or a normative discipline rather than a descriptive discipline. 1. I.e., it prescribes how one ought to reason its not concerned with how one actu aloney does reason. 2. Logic is concerned with laying down the rules for correct reasoning. 3. Consequently, logic seeks to distinguish good arguments from poor ones. II. How Logic helps reasoning A. "Practice makes better." Some examples of how this course thunder mug help reasoning about the world are as follows. 1. Consider this syllogism All pursual of Senator Jones are in favor of higher taxes.All communists are in favor of higher taxes.All followers of Senator Jones a re communists. It will become easy for us to recognize the fallacy in this argument as the fallacy of the undistributed middle term. 2. Consider this promiscuous argument In spite of the large number of UFO spottings that can be attributed to weather conditions and known aircraft and other factors, thither are hundreds of sightings that cannot be accounted for. Hence, we can safely conclude that UFOs exit. Consider this counter-example In spite of the large number of quarters put under kids pillows which can be attributed to sneaky parents, brothers, sisters, and so forth, there are hundreds of cases which cannot be accounted for. Therefore, the tooth fairy exits. B. As well, this course can help with "the negative approach"that we avoid errors by being sure of them, e.g., being aware of common formal and informal fallacies. 1. Consider the passage, "Napoleon became a great emperor be acquire he was so short." In this short argument, the fallacy of false cause (or non causa pro causa) occurs. If this argument were good, all or most short persons would become great emperors. 2. Consider the passage, " large number in developing countries get overaged as an earlier age, because the average life expectancy is so short in those countries." Due to infant mortality, people do not get erstwhile(a) more quickly the fallacy of division occurs. C. Methods, criteria, and t... ...t bottom I did not believe I had touched that man. The law of probabilities decreed me guiltless of his blood, for in all my small experience with guns I had never hit anything I had tried to hit and I knew I had done my best to hit him." 3. Or deal out extrapolation techniques used in stock market prediction, e.g., the wedge formation. V. What logic is not A. Logic is not the intuition of the laws of thought--in which case it would be a descriptive science like psychology. 1. Sometimes people can come to conclusions reliably without being able to know or explai n how the conclusion was reached. E.g., the so-called intuitive eccentric person of personality. 2. Often people can come to the right conclusion for the wrong reasons. Logic is the study of the modes of correct reasoning as shown in an interpersonal manner. B. Logic is not really the science of reasoning either because the logician is not interested in the psychological processes of reasoning. 1. The logician is interested in the structure of arguments. 2. People infer statements and statements entail other statements. 3. We want to say that the entailment is there even though someone does not at this time understand it.

B/M Ratio Essay -- Research Analysis

We can see from regression I of gore A and B that B/M is highly significant congress to the future returns, which are consistent with the B/M effect theory. The B/M effect for NOA/PNOA1 data also cannot be rejected for the ambiguous t-statistic. Similarly, the enterprise B/M ratio is confirmed by regression II. The testing results for one factor vex of financial supplement (ND/M) presents that the financial leverage is not significantly relative to future returns, or even negatively relative to future returns. It is an anomalous phenomenon that the leverage (financial risk) reduces the expected returns, but it may be explained by the negative association between leverage and operating risk. Actually, these results are also consistent with also correlation analysis in Table 2. When NOA/PNOA1, leverage is positively and insignificantly relative to the NOA/PNOA. However, when NOA/PNOA1, leverage is negatively relative to NOA/PNOA. fixing V shows the leverage coefficient under cont rolling for operating risk (enterprise B/M ratio). For full sample tests, the coefficient is insignificant which means we cannot get any sure conclusions from this result. And for NOA/PNOA 1, the coefficient is insignificantly positive, for NOA/PNOA1, the coefficient is significantly negative. If we split the ND/M ratio into financial liabilities/market value and financial assets/ market value worry in regression VII, FA/P coefficients are significantly positive in all of three panels, but the FL/P negatively or insignificantly relative to future returns. It indicates that the high future return premium is awarded for the high operating risk rather than financial risk.As the financial leverage is insignificant in some regressions, we u... ...returns, and this association is even insignificant for most NOA/PNOA portfolios. The only exception is in the highest two NOA/PNOA portfolios, highest ND/M portfolios give higher(prenominal) returns than lowest ND/M portfolios. Similarly w ith US evidence, the findings in UK evidence suggest that the UK FTSE 350 stock returns are inconsistent with PRTs equation 2. In Panel B of table 4, we use similar methods with Panel A to spilt the year-firm observations into 10 portfolios, and sort these observations into five groups by B/M ratio rather than leverage ratio. According to PRT equation 1, if NOA/PNOA 1, a higher leverage ratio will lead to a higher B/M ratio if (NOA )/PNOA 1, a higher return the higher B/M ratio.