Exploring the Chess Discourse Community Essay Example | Topics and Well Written Essays - 1000 words

Exploring the Chess Discourse Community - Essay Example From this paper, it is clear that  the Chess discourse community is comprised of people who have an interest in the game of chess. Chess is a board game that is played by two players who apply different strategies and tactics to beat the opponent. Each player starts with 16 different pieces of knights, rooks, bishops, pawns, a queen, and a king with the main objective being to capture the opponent’s king. The main issues of concern for the chess discourse community are ways to play chess and the opportunity to help each other in developing chess-playing skills. Members of the chess community aim at winning many games and rising to rank within the community.  According to the study findings, the discourse community undertakes the objective of organizing and facilitating chess tournaments between members in the group and chess communities in other institutions. Knowledge in the group constitutes information about ways to play chess. Knowledge is the community is created expe rimentally and through discussion. Members can learn new strategies and tactics by experimenting during games against other members. Members can also gain new knowledge through discussions and conversations with other members.  The chess discourse community utilizes a complex language in interactions between members. During chess tournaments between members, the common words exchanged between the players are â€Å"Checkmate†, â€Å"Check† and â€Å"Adjust†.

Elizbeth Bishop Personal Response Intro Essay Example for Free

Elizbeth Bishop Personal Response Intro Essay The poetry of Elizabeth Bishop appeals to me because she writes about things which are relevant, in a remarkably vivid and vital way. Bishops misfortune in life has inspired her to write distressing poems in which she describes to us the loss she suffered at an early stage in her childhood. She also tells us about her deepest and darkest moments in life. Out of Bishops troubled life, her poetry was born. Bishop has a painters eye and she vividly describes the world around her. She has a keen eye for detail and this is shown in the descriptive language in her poetry. Her strong sense of imagery draws the reader into her poetry. The themes exploded in Bishops poetry have universal appeal. What makes Bishops poetry particularly appealing is her ability to make ordinary everyday objects seem fascinating. Through Bishops poetry we see how close observation leads the poet to have several moments of awareness where she experiences several epiphanies. These moments of awareness are highly dramatic but extremely interesting. The poems ‘Sestina’, ‘First Death In Novia Scotia’, ‘Filling Station’, ‘The Fish, The Armadillo and ‘The Prodigal’ all demonstrate various issues such as nature and childhood memories. What appealed to me most about Bishop’s poetry was her use of striking and powerful imagery. I thoroughly enjoyed ‘The Fish for its unusual imagery and detailed description. I was drawn into the poem immediately as she says, I caught a tremendous fish. She describes the fish as ‘battered’, ‘venerable’ and ‘homely. Bishop then goes on to compare the fish to everyday domestic items. His brown skin hung in strips / like ancient wallpaper. I found this statement particularly appealing as it evoked a sense of homely familiarity in me.

Bilayer Organic Solar Cell in MATLAB

Bilayer Organic Solar Cell in MATLAB Chapter 3 Modelling and Simulation 3.1 Introduction This thesis is based on simulation of design characteristic of bilayer organic solar cell in MATLAB so it is very essential to be familiar with modelling and simulation. This chapter explains about modelling and simulation, characteristics of simulation, mathematical modelling (analytical and numerical both) and its properties, electrical modelling, work done in the field of modelling and simulation of OSC and finally small introduction of MATLAB which shows it’s features because of which this simulation work is in MATLAB. 3.2 Modelling and Simulation Modelling and simulation [1-4] is obtaining related data about how something will act without really trying it in real life. MS is using models either statically or over time, to build up data as a basis for making technical decisions. The terms modelling and simulation are often used interchangeably. Simulation skill is the tool set of engineers of each and every application domains and included in the knowledge body of engineering management. Modelling and simulation is a regulation on its own. With the addition of dynamic factor, simulation systems develop their functionality and allow to calculate predictions, estimates, optimization and what-if analyses. The meaningful abstraction of reality, follow-on in the proper necessity of a conceptualization and fundamental assumptions and constraints, is known as modelling. Simulation is execution of a model over time. Conceptualization is targeted by modelling, means modelling belongs to abstraction level and implementation is targeted by simulation, means simulation belongs to implementation level. Conceptualization (modelling) and implementation (simulation)– are the two activities that are jointly dependent, but can nevertheless be conducted by separate individuals. Modelling and simulation has helped to reduce expenses, enhance the feature of products and systems, and document. 3.2.1 Features of Simulation Interest in simulation applications are increasing gradually because of the following reasons- Use of simulation is cheaper and safer as compared to conduction of experiment. As compared to the conventional experiments, simulations can be more realistic because it permits free formation of surroundings parameters that are obtained in the active application area of the final product. As compared to real time, execution of simulation is faster because of this quality it can be used in if-then-else analysis of unlike alternatives, in particular when the essential information to initialize the simulation can simply be founded from functioning data. Tool box of conventional decision support system is being added a decision support simulation system with the use of simulation. Set up of a coherent synthetic environment is permitted by simulation which allows addition of simulated systems in the premature analysis phase through mixed virtual systems with virtual check surrounding to first prototypical elements for concluded system. If managed perfectly, the surrounding can be migrated from the growth and test domain to the domain of training and learning in resulting life cycle phases for the systems. 3.2.2 Steps for Modelling For modelling four basic steps are as follows †¢ Step 1: Monitor – In the first step conceptual model of ground profile and job objectives are developed. †¢ Step 2: Measure – In the second step theoretical model is developed which is used to explain the main processes running in the problem. †¢ Step 3: Describe – In the third step mathematical explanation of these processes are developed and to get a perfect solution verification is also done. †¢ Step 4: Verify – In the fourth step under the light of experimental physical reality, results of mathematical expression is interpretated. Confirm the suggestion, get additional measurements, enhance the complexity or precision of the mathematical result, or modify your conceptual understanding until you have complete understanding of the physical actuality. 3.3 Mathematical Modelling Fig 3.1, shows the simplest explanation of modelling – the method through which we can take out a complex physical actuality from a suitable mathematical reality on which designing of system is based. Development of suitable mathematical expression is done in numerical modelling. Mathematical modelling is a group of mathematical expressions that show the variation of a system from one state to another state (differential equations) and dependence of one variable to the other variable (state equations). The use of mathematical words to describe the performance of a system is mathematical modelling. Performance of photovoltaic system [5-7] is also illustrated by mathematical modelling. Number of different parameters (like – series and shunt resistance, ideality factor, reverse saturation current, open circuit voltage, short circuit current, fill factor, photo-generated current, efficiency) of photovoltaic system can be calculated by mathematical modelling. Fig. 3.1: Simple definition of modelling. 3.3.1 Properties of Mathematical Modelling We prefer mathematical modelling because of the following reasons With the help of mathematical model we can understand and investigate the meaning of equations and useful relations. It becomes very simple to make a educational environment in which preliminary person can be interactively occupied in guided inquiry and hands on actions with the help of mathematical modelling software (like – Stella II, Excel, online JAVA, MATLAB). Mathematical model is build up after the development of conceptual model of physical system. It is used to calculate approximately the quantitative presentation of the system. In order to spot a model’s strengths and weaknesses, quantitative outcomes obtained from mathematical modelling can be compared with observational information. The most important element of the resultant â€Å"complete model† of a system is mathematical model. Complete model is an assembly of theoretical, physical, numerical, visualization and statistical sub-models. 3.4 Types of Mathematical Modelling These can also be divided into either numerical models and analytical models. 3.4.1 Numerical Modelling – It is one of the type of mathematical modelling in which numerical time stepping method is used to obtain model response over time. Results are presented in the form of graph or table. In this thesis numerical modelling is used to analysis the design characteristic of Bilayer Organics Solar Cell. 3.4.2 Analytical Modelling – Modelling having a closed form results is called analytical modelling. In closed form results, mathematical analytic functions are used to present the response to the equations that describe variation in a system. 3.5 Electrical Modelling In this section, the electrical model for bilayer organic solar cell is described. One of the important characteristics of organic materials is their extremely small mobility, which makes modelling of their electrical properties difficult. Another problem in the electrical modelling of organic thin film devices (e. g. planar organic solar cells) was the lack of unique and precise electrical parameters for very thin layers of materials and occasionally lack of any information. Here with the aid of a self consistent loop between the Poisson equation and continuity equations for electrons and holes, the I-V curve of the device is calculated. It is assumed that the electrical current is due to the drift-diffusion transport of carrier. Consequently, in order to model the drift diffusion equations, a self consistent loop between the solutions of Poissons equation and two separate continuity equations for electrons and holes is needed. The design of the loop should be in a way such that the solution of each equation can be used as the initial conditions for the others, to generate a self correcting mechanism. The model that is used is based on the following assumptions: The generated excitons are separated right after absorption and the numbers of the generated electron-hole pairs are directly imported into the continuity equations as the generation rate . The transport properties of the organic materials can be totally modelled by mobility, DOS, bimolecular recombination term and doping levels. The connections between different layers follow the physical rules of hetero-junction connections between conventional semiconductors interfaces. The other two equations, which are solved in a closed loop with the mentioned Poisson equation, are two separate continuity equations one for the electrons and one for the holes. The flowchart of the electrical model using the mentioned equations is shown in Fig. 3.2. Fig. 3.2 : Flowchart of electrical model. 3.6 Work Done in Modelling and Simulation of OSC Pettersson et al (1999)[8] have reported a model based on the experimental short circuit light generated current action spectrum of poly(3-(4’-(1†,4†,7†-trioxaoctyl)phenyl)thiophene) (PEOPT)/C60 fullerene hetero-junction photovoltaic devices. This modelling was completely based on the assumption that generation process of photocurrent is the result of creation, diffusion and dissociation of excitons. Using complex refractive indices and layer thickness, internal optical electric field was computed. We got values for exciton diffusion length of 4.7 and 7.7 nm for PEOPT C60 respectively. Computed photocurrent and electric field distribution were used to study the effect of geometrical architecture with respect to the efficiency of device. Cheknane et al (2007)[9] has reported a photovoltaic cell in which photo-active layer of MDMO-PPV and PCBM material is sandwiched between ITO and Al electrodes, there is an additional interfacial layer of PEDOT/PSS on the top of ITO. Comparision between V-I characteristics of device with and without extra interfacial layer is done and modelled by electrical equivalent circuit. Simulation results show that V-I characteristics of bulk hetero-junction solar cell is affected by extra interfacial layer of PEDOT/PSS. Hwang et al (2007)[10] has reported drift-diffusion time dependent model of OSC based on blends of P3HT and red polyfluorene copolymer. In this model electron trapping and field dependent charge separation is used to investigate the device physics. This model is used to reproduce practical light-generated current transients observed in response to variable intensity step function excited light. Vervisch et al (2011)[11] has reported OSCs simulation using finite element method. Using finite difference time domain process, optical modelling is done and electrical characteristics is obtained by solving Poisson’s and continuity equations. Simulation results show the effect of physical parameters like exciton lifetime on OSC performance. Casalegno et al (2013)[12] has reported numerical approaches that give valuable information of microscopic processes underlying generation of photo-current in OSC. Here 3D master equation approach is used in which equations explaining particle dynamics rely on mean field guess and result is obtained numerically. Reliability of this method is tested against Kinetic Monte Carlo simulation method. V-I curve shows that the result of this method is very close to the exact result. Because of the adoption of mean field approximation for electrostatic interactions, we get biggest deviation in current densities. Strong energy disorder can also affect response quality. Simulation results show that master equation approach is faster than Kinetic Monte Carlo approach. Foster et al (2013)[13] presented a drift-diffusion model to obtain V-I curves and equivalent circuit parameters of bilayer organic solar cell. Minority carrier densities are neglected and final equations are solved with internal boundary condition on material interface and ohmic boundary condition on contacts. From the solution of this model V-I curves are calculated. 3.7 Introduction to MATLAB MATLAB [13] is a high performance language for technical computing. It integrates calculation, visualization and programming in a simple to use surroundings where troubles and solutions are presented in well-known mathematical notation. MATLAB can solve technical computing troubles faster than conventional programming language (like – Forton, C, C++). Typical uses include – Financial modeling and investigation Computational biology Math and computation algorithm development Data acquisition modeling Simulation and prototyping data study Exploration and visualization Graphics application development for scientific and engineering field Graphical user interface building Matrix laboratory is the full form of MATLAB. Basic data element in MATLAB is an array which does not need dimensioning. With the help of MATLAB number of technical computing troubles mainly those with vector and matrix formulations can be solved in a fraction of time. Basically it was written to give simple access to matrix software. For advance science, mathematics, engineering field and high productivity industrial research, progress and study MATLAB is very important instruction tool. Comprehensive collection of MATLAB functions are toolbox. Toolboxes of MATLAB permit us to study and apply specific technology. Toolboxes are available in different areas like – neural network, communication, signal processing, fuzzy logic, simulation, control system and many others. Differential equations are solved very easily in MATLAB [14-17]. We can also do modeling and simulation of solar cell using MATLAB [18,19]. 3.8 Conclusions This chapter explains about modelling and simulation. Presentation of physical configuration or activities of device by conceptual mathematical model that approximates this behavior, is called modeling. Model may either be closed form equation or arrangement of simultaneous equations that are numerically solved. Analytical and numerical both type of analysis can be used in modeling. Simulation is process of imitating the physical system behavior by considering the characteristic of an analogous but different system without resorting direct practical experimentation. For simulation we are using MATLAB which is a high performance technical computing language. We get that MATLAB integrates calculation, programming and visualization in a simple to use surroundings where mathematical expressions are used to express troubles and solutions. Because of all these qualities of MATLAB a system of number of numerical equations used for electrical modelling of bilayer organic solar cell are solved easily and in better way as compared to other programming languages. 3.9 References [1] B. P. Zeigler, Wiley, New York, (1976). [2] A. M. Law and W.D. Kelton, 2nd ed., McGraw-Hill,  New York, (1991). [3] F. Haddix, Paper 01F-SIW-098, Proceedings of the Simulation Interoperability Workshop, Fall (2001). [4] A. Crespo-Mà ¡rquez, R. R. Usano and R. D. Aznar, Proceedings of International System Dynamics Conference, Cancun, Mexico, The System Dynamics Society, (1993), 58. [5] J. S. Kumari and C. S. Babu, International Journal of Electrical and Computer Engineering (IJECE), 2(1), (2012), 26-34. [6] P. Sudeepika, G.Md. G. Khan, International Journal of Advanced Research in Electrical,Electronics and Instrumentation Engineering, 3(3), (2014), 7823-7829. [7] M. Abdulkadir, A. S. Samosir, A. H. M. Yatim, International Journal of Power Electronics and Drive System (IJPEDS), 3(2), (2013), 185-192. [7] L. A. A. Pettersson, L. S. Roman, and O. Ingana, Journal of Applied Physics, 86, (1999), 487-496. [8] A. Cheknane, T. Aernouts, M. M. Boudia, ICRESD-07, (2007), 83 – 90. [9] I. Hwang, C. R. M. Neill, and N. C. Greenham, Journal of Applied Physics, 106, (2009), 094506:1-10. [10] W. Vervisch, S. Biondo, G. Rivià ¨re, D. Duchà ©, L. Escoubas, P. Torchio, J. J. Simon, and J. L. Rouzo, Applied Physics Letters, 98, (2011), 253306:1-3. [11] M. Casalegno, A. Bernardi, G. Raos, J. Chem. Phys., 139(2), (2013). [12] J. M. Foster, J. Kirkpatrick, and G. Richardson, Journal of Applied Physics, 114, (2013), 104501:1-15. [13] A. Knight, CRC Press LLC, (2000). [14] R. K. Maddalli , Indian Journal of Computer Science and Engineering, 3(3), (2012), 406-10. [15] Z. M. Kazimovich and S. Guvercin, International Journal of Computer Applications, 41(8), (2012), 1-5. [16] A. B. Kisabo, A. C. Osheku, A. M. Adetoro, A. Lanre and A. Funmilayo, International Journal of Scientific and Engineering Research, 3(8), (2012), 1-7. [17] V. Nehra, I.J. Intelligent Systems and Applications, 05, (2014), 1-24. [18] S. Nema, R. K. Nema, and G. Agnihotri, International Journal of Energy and Environment, 1(3), (2010), 487500. [19] M. Edouard, D. Njomo, International Journal of Emerging Technology and Advanced Engineering, 3(9), (2013), 24-32.

Language Grows Out of Life:Abduction, Juxtaposition, and Culture :: Education Learning Essays

Language Grows Out of Life: Abduction, Juxtaposition, and Culture Language grows out of life, out of its needs and experiences . . . Good work in language presupposes and depends on real knowledge of things. I never taught language for the purpose of teaching it; but invariably used language as a medium for communication of thought: thus the learning of language was coincident with the acquisition of knowledge (Thomas, 48). For my students in the prison, and for many students in "regular" schools, English class seems removed from the "needs and experiences" of life. My students are confused by the isolated teaching of grammar rules that seem to have no impact on their "true" use of language on the streets, in their neighborhoods, or with their families. I am equally confused. Many schools insist that teachers "transmit" a pre-determined body of information to students as if they are receptacles. For my students, many of the works of literature in this body of information are "unrealistic," and they feel they are "fake" and unimportant to them. The schools also often ask instructors to ignore their students' cultures and social circumstances. This is an impossibility. Donald Thomas states this nicely when he writes: "We bequeath to words what we cannot ourselves decipher from the rush of daily being. Words are juxtaposed to the world just as we are" (2). Simply put, culture and language are interconnecte d. We strive to make sense of the world around us through language. There is no way to separate culture and language and no reason to do so. I become more aware of how experience affects language and expression each day. Several months ago, I was working with my students on the use of setting in literary works. I tore pictures of different settings from National Geographic magazines. My students had a huge range of pictures to choose from and their assignment was to write a story that would logically take place in the setting of their picture. We had been discussing literature genres and I was sure that the student who chose the picture of the mist-surrounded castle on the rocky island would create a magical fairy tale complete with a king, knights, and a fire-breathing dragon. I was wrong. I have read many fairy tales in my lifetime. If I had received the picture of the castle, I would have written a "typical" fairy tale. My home culture has nurtured this type of story and appreciation for it.

Technology Has Changed the Live of Teen Agers

DOI: 10. 1111/j. 1464-5491. 2006. 01868. x Glycaemic control Review Article 23 0742-3071Publishing, alcohol Diabetic Medicine and2006 consumption D. Ismail et al. DME UK Oxford, article Blackwell Publishing Ltd Social consumption of alcohol in adolescents with Type 1 diabetes is associated with increased glucose lability, but not hypoglycaemia D. Ismail, R. Gebert, P. J. Vuillermin, L. Fraser*, C. M. McDonnell, S. M. Donath†  and F. J. Cameron AbstractDepartment of Endocrinology and Diabetes, Royal Children’s Hospital, Melbourne, *Wimmera Base Hospital*, Horsham and † Clinical Epidemiology and Biostatistics Unit, Royal Children’s Hospital, Melbourne, Australia Accepted 10 June 2005 Aims To determine the effects of social consumption of alcohol by diabetic adolescents on glycaemic control. Methods Fourteen (five male) patients aged > 16 years were recruited from the diabetes clinic at the Royal Children’s Hospital. The continuous glucose monitoring syste m (CGMS) was attached at a weekend when alcohol consumption was planned for one night only.For each patient, the 12-h period from 18. 00 h to 06. 00 h for the night with alcohol consumption (study period) was compared with the same period with non-alcohol consumption (control period) either 24 h before or after the alcohol study night. Thus, each subject was his /her own control. Glycaemic outcomes calculated from continuous glucose monitoring included mean blood glucose (MBG), percentage of time spent at low glucose levels (CGMS < 4. 0 mmol/l), normal glucose levels (CGMS 4. 0–10. 0 mmol/ l) and high glucose levels (> 10. mmol/ l) and continuous overall net glycaemic action (CONGA). Results The mean number of standard alcohol drinks consumed during the study period was 9. 0 for males and 6. 3 for females. There was no difference in percentage of time at high and normal glucose levels in the study and control periods. During the control period, there was a higher percentage o f time with low glucose levels compared with the study period (P < 0. 05). There was an increased level of glycaemic variation during the study time when compared with the control period.Conclusions In an uncontrolled, social context, moderately heavy alcohol consumption by adolescents with Type 1 diabetes appears to be associated with increased glycaemic variation, but not with low glucose levels. Diabet. Med. 23, 830–833 (2006) Keywords adolescence, alcohol, glycaemic control Abbreviations CGMS, continuous glucose monitoring system; CONGA, continuous overall net glycaemic action; MBG, mean blood glucose; RCH, Royal Children’s Hospital Introduction Adolescents with Type 1 diabetes frequently engage in risk-taking activities [1].Amongst these activities is the social Correspondence to: Dr Fergus Cameron, Deputy Director, Department of Endocrinology and Diabetes, Royal Children’s Hospital, Flemington Road, Parkville, Victoria 3052, Australia. E-mail: fergus. [ema il  protected] org. au consumption of alcohol, frequently as underage drinkers [2]. Whilst the effects of alcohol consumption upon glycaemia have been well described in a controlled setting [3– 6], little is known about the impact on glucose levels of alcohol consumption by adolescents within an ambulant, social context.The purpose of this project was to utilize continuous glucose monitoring to study the impact of social alcohol consumption on glycaemic control in a group of alcohol-using adolescents.  © 2006 The Authors. 830 Journal compilation  © 2006 Diabetes UK. Diabetic Medicine, 23, 830–833 Review article 831 Patients and methods This study was approved by the Human Ethics Research Committee of the Royal Children’s Hospital (RCH). That approval was contingent upon the fact that the investigators should not be seen to encourage underage drinking in adolescents.Consequently, we only approached adolescents who we knew were drinking socially and, despite our previous counselling, elected to continue to drink alcohol on a semi-regular basis. We recruited 22 adolescents with Type 1 diabetes from the RCH diabetes clinic. The adolescents were considered eligible only if > 16 years old and parental/patient consent was obtained. HbA 1c (Bayer DCA 2000 immunoagglutination method, Calabria, Barcelona, Spain) was measured, and diabetes duration and insulin doses were recorded. The MiniMed continuous glucose monitoring system (CGMS) was attached to the study patients over a weekend period.Patients were required to have an alcohol-free period for at least 24 continuous hours during the weekend trace period. A diary was kept of activities during the trace period (insulin injections, meal, snacks, dancing, alcohol consumption, sport). There was no change in insulin doses between study and control periods. In the evening when alcohol was consumed, patients were asked to recall how many and what type of drinks were consumed and how inebriated the y became. Patients recall of alcohol consumption was converted to ‘standard drinks’ (one standard drink contains the equivalent of 12. ml 100% alcohol) using The Australian Alcohol Guidelines [7]. CGMS data was recorded between 18. 00 and 06. 00 h on the evening when alcohol was consumed (the study period) and between 18. 00 and 06. 00 h on the evening when no alcohol was consumed (the control period). CGMS data were only analysed if there had been regular calibrations with intermittent capillary blood glucose readings at a maximum of 8-h intervals. Each CGMS trace was qualitatively and quantitatively analysed using mean glucose values, per cent time in glycaemic ranges and ontinuous overlapping net glycaemic action (CONGA) [8]. CONGA values were calculated to assess glycaemic variation over 1-, 2- and 4-h intervals. Low glucose values were defined as CGMS values < 4 mmol/ l, normal glucose values when CGMS values were 4– 10 mmo/ l and high glucose values when CG MS values were > 10 mmol/ l. Each patient acted as their own control with study periods and control periods being compared. Inter-individual values were grouped for comparison. Differences between study and control periods were analysed using paired t-tests. Analyses were done in Stata [9]. ales and nine females. The mean age was 18. 5 years (range: 17. 4 – 19. 5). The mean duration of diabetes was 9. 4 years (range: 3 – 16. 3). Six of our subjects took four insulin injections per day and eight took two injections daily. The mean insulin dose was 1. 1 units /kg/day (range: 0. 7 –1. 8), and the mean HbA1c was 9. 6% (range: 8. 2 – 10. 8). Activities during the study period Thirteen subjects had dinner before drinking and only one subject did not consume any food before going out. Three subjects ‘danced a lot’ and six subjects went dancing but did not dance a lot.Ten subjects had something to eat after drinking. Alcohol consumption during the st udy period The mean number of alcohol drinks consumed on the study night was 9. 0 (range 3–16) for males and 6. 3 (range 3–14) for females. All the females consumed pre-mixed sweetened alcohol drinks (5% alcohol), with only one consuming beer and one consuming wine. Four of the males consumed mixed spirits, one mixed spirits and beer and one beer only. Forty per cent of the males had more than seven standard drinks during the study and 67% of the females had more than five drinks.In total, 80% of the subjects had pre-mixed sweetened alcohol drinks at some point during the study period. Forty-three per cent of the subjects reported that they became inebriated and 14. 3% consumed alcohol to the point where they became physically sick. None of the subjects lost consciousness or took recreational drugs during the study period. Comparative CGMS data between study and control periods Results Patients There was no significant difference between the overall mean glucose levels of patients when comparing study and control periods (Table 1; P = 0. 43).Similarly, there were no significant differences in the amount of time spent with either normal or high glucose values between study and control periods (Table 1). A larger proportion of time was spent with low glucose values during the control period when compared with the study period (1. 9 vs. 16. 8%, P = 0. 03). A significantly larger degree of glycaemic variation was seen in the CONGA values in the study period when compared with the control period (Table 1). The difference in CONGA values were consistent and independent of whether glycaemic variation was assessed over 1-, 2- or 4-h intervals.Of the 22 subjects recruited, eight were excluded because their CGMS traces did not have sufficiently frequent calibration points with intermittent capillary measures of blood glucose. Of the 14 subjects remaining, we were able to obtain study period data on 14 patients and matched control period data on only 12 pat ients. The study period occurred on the night prior to the control period in nine subjects. There were five Discussion It has long been recognized that a prohibitionist approach is usually ineffective when counselling adolescents who engage in risk-taking behaviours [10].Many centres today, ourselves included, have instead adopted a harm minimization approach in dealing with such behaviours. An important component  © 2006 The Authors. Journal compilation  © 2006 Diabetes UK. Diabetic Medicine, 23, 830–833 832 Glycaemic control and alcohol consumption †¢ D. Ismail et al. Outcome measure Mean difference between Study period Control period study period and mean value mean value control period (95%CI) P-value 10. 6 16. 8 58. 6 24. 6 2. 1 3. 2 3. 7 1. 2 (? 2. 1, 4. 4) ? 14. 9 (? 28. 1, ? 1. 8) ? 0. 8 (? 27. 3, 25. 8) 15. 7 (? 4. 5, 35. 8) 0. 6 (0. 2, 1. 0) 1. 1 (0. , 1. 9) 1. 8 (0. 4, 3. 1) 0. 43 0. 03 0. 95 0. 12 0. 006 0. 01 0. 01 Table 1 CGMS outcomes, study and contro l periods Blood glucose levels (mmol/l) 11. 8 Per cent time low glucose 1. 9 Per cent time high glucose 57. 8 Per cent time normal glucose 40. 3 CONGA1* 2. 7 CONGA2* 4. 3 CONGA4* 5. 5 *CONGA calculated at 1-, 2- and 4-h intervals. CONGAn is the standard deviation of different glucose measures n hours apart for the duration of the CGMS trace. of counselling using a harm minimization approach is that the information provided be credible and reflective of ‘real’ or ‘lived’ circumstances.Continuous glucose monitoring provides a technique whereby the glycaemic consequences of various behaviours can be documented in an ambulant or non-artificial setting. Adolescents with Type 1 diabetes frequently consume alcohol in a social context [11]. Alcohol is known to inhibit the gluconeogenic pathway, to inhibit lipolysis, impair glucose counter-regulation and blunt hypoglycaemia awareness [3,4]. Previous studies in young adults with Type 1 diabetes have shown that modera te consumption of alcohol in the evenings without concomitant food intake may cause hypoglycaemia the following morning [5].Consumption of alcohol after a meal, however, has shown no similar adverse effects on glucose [6]. It is reasonable to assume, therefore, that alcohol consumption may be a significant risk factor for hypoglycaemia in adolescents with Type 1 diabetes [5]. Studies of the glycaemic effects of alcohol consumption in an ambulant adolescent/young adult population can be difficult. This is because such behaviours are uncontrolled, often spontaneous and usually in the context of other social activities (parties, dancing, etc. ).In order to ensure that we only reported accurate CGMS data during these activities, capillary blood glucose calibration was considered vital and those patients who failed in this regard were excluded from analysis. Just over 60% of the patients recruited were able to successfully wear and calibrate a CGMS unit during these activities. Given tha t patients who experience hypoglycaemic symptoms are more likely to perform capillary self measures of blood glucose, we feel that it is unlikely that those patients excluded from the analysis had a greater frequency of hypoglycaemia than those patients reported.We were unable to record our subjects’ alcohol consumption in a contemporaneous fashion and hence were reliant upon their recall. It is possible that their remembered patterns of consumption were not entirely accurate. This potential inaccuracy should not be seen as a weakness of this study, as we only set out to determine patterns of glycaemia in adolescents engaging in spontaneous and uncontrolled alcohol consumption. We neither specified the type nor the amount of alcohol to be consumed (our ethical approval was contingent on this not occurring).The data as to amount of alcohol consumed have been included for descriptive purposes only. The results of this study show that alcohol consumption by adolescents in a soci al context is associated with a greater degree of glycaemic variation and less time spent with low glucose values than evenings where no alcohol is consumed. Whilst the second of these findings appears counter-intuitive, there may be several possible explanations. Firstly, the vast majority of our study group ate a meal prior to going out and ate upon their return before going to bed.These are practices that we have instilled as harm minimization strategies to avoid alcohol-induced hypoglycaemia in our clinic. Secondly, most of the alcohol consumed was as pre-mixed spirit and sweetened, carbonated beverages. Finally, alcohol consumption was only associated with vigorous exercise (dancing) in a minority of our study group. All of these factors could have combined to negate the hypoglycaemic effects of alcohol. In a previous study of glycaemia during alcohol consumption in adult men [5], hypoglycaemia occurred most often 10–12 h after wine consumption when the evening before en ded at 23. 0 h. We analysed our data to see if a similar phenomenon occurred in this study and found that the per cent of time spent with CGMS readings < 4 mmol/l between 06. 00 and 12. 00 h on the morning after the study period (i. e. the morning after the drinking night) was only 1. 1%. Notwithstanding the fact that our cohort frequently consumed alcohol later than 23. 00 h, the factors that impacted upon glycaemic control during the study night appear to have carried over to the ‘morning after’. The findings in this study highlight the importance of ambulant testing.It is important to note that the findings of the group studied here may not be seen in adolescents who drink non-sweetened alcoholic drinks or in those adolescents with better underlying metabolic control. Whilst alcohol consumption in isolation may reasonably be thought to cause hypoglycaemia, alcohol consumption by adolescents in the context of meals, sweetened mixers and little activity did not result in more hypoglycaemia than an alcohol-free evening. Whether the increase in glycaemic variation seen on an evening  © 2006 The Authors. Journal compilation  © 2006 Diabetes UK.Diabetic Medicine, 23, 830–833 Review article 833 of alcohol consumption has negative clinical outcomes remains an area for further investigation. Competing interests CMM was a Novo Nordisk research fellow. FJC received fees for speaking at conferences and funds for research from Novo Nordisk. References 1 Cameron F, Werther G. Adolescents with diabetes mellitus. In: Menon, RK, Sperling, MA, eds. Pediatric Diabetes. Boston: Kluwer Academic Publishers, 2003: 319–335. 2 Frey MA, Guthrie B, Lovelandcherry C, Park PS, Foster CM. Risky behaviours and risk in adolescents with IDDM.J Adol Health 1997; 20: 38–45. 3 Avogaro A, Beltramello P, Gnudi L, Maran A, Valerio A, Miola M et al. Alcohol intake impairs glucose counterregulation during acute insulin-induced hypoglycaemia in IDDM patients. D iabetes 1993; 42: 1626–1634. 4 Kerr D, Macdonald IA, Heller SR, Tattersal RB. Alcohol causes hypoglycaemic unawareness in healthy volunteers and patients with type 1 diabetes. Diabetologia 1990; 33: 216–221. 5 Turner BC, Jenkins E, Kerr D, Sherwin RS, Cavan DA. The effect of evening alcohol consumption on next morning glucose control in type 1 diabetes.Diabetes Care 2001; 24: 1888–1893. 6 Koivisto VA, Tulokas S, Toivonen M, Haapa E, Pelkonen R. Alcohol with a meal has no adverse effects on postprandial glucose homeostasis in diabetic patients. Diabetes Care 1993; 16: 1612–1614. 7 National Health and Medical Research Council. Australian Alcohol Guidelines: Health Risks and Benefits. DS9. Available from: http://www7. health. gov. au/nhmrc/publications/synopses/ds9syn. htm. 8 McDonnell CM, Donath SM, Vidmar SI, Werther GA, Cameron FJ. A novel approach to continuous glucose analysis utilising glycaemic variation.Diab Tech Therap 2005; 7: 253–263. 9 Sta taCorp. Stata statistical software. Release 8. 0. College Station, TX: Stata Corporation, 2003. 10 Kyngas H, Hentinen M, Barlow JH. Adolescents perceptions of physicians, nurses, parents and friends: help or hindrance in compliance with diabetes self-care? J Adv Nurs 1998; 27: 760–769. 11 Patterson JM, Garwick AW. Coping with chronic illness. In: Werther, GA, Court, JM, eds. Diabetes and the Adolescent. Melbourne: Miranova Publishers 1998, 3–34.  © 2006 The Authors. Journal compilation  © 2006 Diabetes UK. Diabetic Medicine, 23, 830–833

Titanosaurus Facts and Figures

Titanosaurus Facts and Figures Name: Titanosaurus (Greek for Titan lizard); pronounced tie-TAN-oh-SORE-us Habitat: Woodlands of Asia, Europe, and Africa Historical Period: Late Cretaceous (80-65 million years ago) Size and Weight: About 50 feet long and 15 tons Diet: Plants Distinguishing Characteristics: Short, thick legs; massive trunk; rows of bony plates on the back About Titanosaurus Titanosaurus is the signature member of the family of dinosaurs known as titanosaurs, which were the last sauropods to roam the earth before the K/T Extinction 65 million years ago. Whats odd is that, although paleontologists have discovered plenty of titanosaurs- the remains of these giant beasts have been dug up all over the globe- theyre not so sure about the status of Titanosaurus: this dinosaur is known from very limited fossil remains, and to date, no one has located its kull. This seems to be a trend in the dinosaur world; for example, hadrosaurs (duck-billed dinosaurs) are named after the extremely obscure Hadrosaurus, and the aquatic reptiles known as pliosaurs are named after the equally murky Pliosaurus. Titanosaurus was discovered very early in dinosaur history, identified in 1877 by paleontologist Richard Lydekker on the basis of scattered bones unearthed in India (not normally a hotbed of fossil discovery). Over the next few decades, Titanosaurus became a wastebasket taxon, meaning that any dinosaur that even remotely resembled it wound up being assigned as a separate species. Today, all but one of these species have either been downgraded or promoted to genus status: for example, T. colberti is now known as Isisaurus, T. australis as Neuquensaurus, and T. dacus as Magyarosaurus. (The one remaining valid species of Titanosaurus, which still remains on very shaky ground, is T. indicus.) Lately, titanosaurs (but not Titanosaurus) have been generating headlines, as bigger and bigger specimens have been discovered in South America. The largest dinosaur yet known is a South American titanosaur, Argentinosaurus, but the recent announcement of the evocatively named Dreadnoughtus may imperil its place in the record books. There are also a few as-yet-unidentified titanosaur specimens that may have been even bigger, but we can only know for sure pending further study by experts.