September 20, 2012 Object Oriented Modeling The aim of this lecture is to illust

September 20, 2012 Object Oriented Modeling The aim of this lecture is to illust www.phwiki.com

September 20, 2012 Object Oriented Modeling The aim of this lecture is to illust

Kiley, Tim, Assistant News Director has reference to this Academic Journal, PHwiki organized this Journal September 20, 2012 Object Oriented Modeling The aim of this lecture is to illustrate the requirements of a software environment as long as object-oriented modeling of physical systems in addition to to show how these requirements can be met in practice. The lecture offers a first glimpse at features in addition to capabilities of Dymola, a software environment created as long as the purpose of modeling complex physical systems in an object-oriented fashion. Dymola offers a graphical user interface. Some features of the underlying textual model representation, called Modelica, are also introduced. Table of Contents The causality of the model equations Graphical modeling Model structure in Modelica Model topology in Modelica Inheritance rules Hierarchical modeling September 20, 2012 The Causality of the Model Equations September 20, 2012

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Basic Requirements of OO Modeling Physical objects should be representable by mathematical graphical objects. The graphical objects should be topologically connectable. The mathematical models should be hierarchically describable. To this end, it must be possible to represent networks of coupled objects again as graphical objects. September 20, 2012 An Example model Circuit1 SineVoltage U0(V=10, freqHz=2500); Resistor R1(R=100); Resistor R2(R=20); Capacitor C(C=1E-6); Inductor L(L=0.0015); Ground Ground; equation connect(U0.p, R1.p); connect(R1.n, C.p); connect(R2.p, R1.n); connect(U0.n, C.n); connect(Ground.p, C.n); connect(L.p, R1.p); connect(L1.n, Ground.p); connect(R2.n, L.n); end Circuit1; Dymola Modelica September 20, 2012 Graphical In as long as mation (Annotation) package CircuitLib annotation (Coordsys( extent=[0, 0; 504, 364], grid=[2, 2], component=[20, 20])); model Circuit1 annotation (Coordsys( extent=[-100, -100; 100, 100], grid=[2, 2], component=[20, 20])); Modelica.Electrical.Analog.Sources.SineVoltage U0(V=10, freqHz=2500) annotation (extent=[-80, -20; -40, 20], rotation=-90); Modelica.Electrical.Analog.Basic.Resistor R1(R=100) annotation (extent=[ -40, 20; 0, 60], rotation=-90); Modelica.Electrical.Analog.Basic.Capacitor C(C=1E-6) annotation (extent=[-40, -60; 0, -20], rotation=-90); Modelica.Electrical.Analog.Basic.Resistor R2(R=20) annotation (extent=[0, -20; 40, 20]); Modelica.Electrical.Analog.Basic.Inductor L(L=0.0015) annotation (extent=[40, 20; 80, 60], rotation=-90); Modelica.Electrical.Analog.Basic.Ground Ground annotation (extent=[0, -100; 40, -60]); equation connect(U0.p, R1.p) annotation (points=[-60, 20; -60, 60; -20, 60], style(color=3)); connect(R1.n, C.p) annotation (points=[-20, 20; -20, -20], style(color=3)); connect(R2.p, R1.n) annotation (points=[0, 0; -20, 0; -20, 20], style(color=3)); connect(U0.n, C.n) annotation (points=[-60, -20; -60, -60; -20, -60], style(color=3)); connect(Ground.p, C.n) annotation (points=[20, -60; -20, -60], style(color=3)); connect(L.p, R1.p) annotation (points=[60, 60; -20, 60], style(color=3)); connect(L.n, Ground.p) annotation (points=[60, 20; 60, -60; 20, -60], style(color=3)); connect(R2.n, L.n) annotation (points=[40, 0; 60, 0; 60, 20], style(color=3)); end Circuit1; end CircuitLib; September 20, 2012

Models in Modelica Models in Modelica consist of a description of their model structure as well as a description of their embedding in the model environment: model Model name Description of the model embedding; equation Description of the model structure; end Model name; September 20, 2012 Model Structure in Modelica The model structure in Modelica consists either of a set of equations, a description of the model topology, or a combination of the two types of model structure descriptions. A topological model description is usually done by dragging in addition to dropping model icons from graphical model libraries into the modeling window. These models are then graphically interconnected among each other. The stored textual version of the topological model consists of a declaration of its sub-models (model embedding), a declaration of its connections (model structure), as well as a declaration of the graphical description elements (annotation). September 20, 2012 Model Topology in Modelica model MotorDrive PI controller; Motor motor; Gearbox gearbox(n=100); Shaft Jl(J=10); Tachometer wl; equation connect(controller.out, motor.inp); connect(motor.flange , gearbox.a); connect(gearbox.b , Jl.a); connect(Jl.b , wl.a); connect(wl.w , controller.inp); end MotorDrive; September 20, 2012

Resistors in Modelica model Resistor “Ideal resistor” Pin p, n; Voltage u; parameter Resistance R; equation u = p.v – n.v; p.i + n.i = 0; Rp.i = u; end Resistor; September 20, 2012 Similarity Between Different Elements model Resistor “Ideal resistor” Pin p, n; Voltage u; parameter Resistance R; equation u = p.v – n.v; p.i + n.i = 0; Rp.i = u; end Resistor; model Capacitor “Ideal capacitor” Pin p, n; Voltage u; parameter Capacitance C; equation u = p.v – n.v; p.i + n.i = 0; Cder(u) = p.i; end Capacitor; September 20, 2012 Partial Models in addition to Inheritance September 20, 2012

Decomposition in addition to Abstraction September 20, 2012 Heterogeneous Modeling Formalisms September 20, 2012 Simulation in addition to Animation Modeling Window Animation Window September 20, 2012

References Brück, D., H. Elmqvist, H. Olsson, in addition to S.E. Mattsson (2002), “Dymola as long as Multi-Engineering Modeling in addition to Simulation,” Proc. 2nd International Modelica Conference, pp. 55:1-8. Otter, M. in addition to H. Elmqvist (2001), “Modelica: Language, Libraries, Tools, Workshop, in addition to EU-Project RealSim,” Modelica web-site. September 20, 2012

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Where in the World  Human in addition to Computer Geolocation of Images James Hays in addition to Alexei A. Efros, Carnegie Mellon University Geolocation estimated from matching scenes via: scene gist descriptor line statistics color histogram texton histogram im2gps Human Geolocation Test Set of Assorted Geotagged Photos

Where in the World  Human in addition to Computer Geolocation of Images James Hays in addition to Alexei A. Efros, Carnegie Mellon University Geolocation estimated from matching scenes via: scene gist descriptor line statistics color histogram texton histogram im2gps Human Geolocation Test Set of Assorted Geotagged Photos www.phwiki.com

Where in the World  Human in addition to Computer Geolocation of Images James Hays in addition to Alexei A. Efros, Carnegie Mellon University Geolocation estimated from matching scenes via: scene gist descriptor line statistics color histogram texton histogram im2gps Human Geolocation Test Set of Assorted Geotagged Photos

Devine, Caribe, Meteorologist has reference to this Academic Journal, PHwiki organized this Journal Where in the World  Human in addition to Computer Geolocation of Images James Hays in addition to Alexei A. Efros, Carnegie Mellon University Geolocation estimated from matching scenes via: scene gist descriptor line statistics color histogram texton histogram im2gps Human Geolocation Test Set of Assorted Geotagged Photos Images with Greatest Per as long as mance Disparity Humans Better Im2gps Better Database of 6.5 million geotagged Flickr photos 20 participants were shown 64 photos in addition to asked to guess the location where each photo was taken. The first set of 32 images allow unlimited viewing. The final 32 images are flashed as long as only 100 milliseconds. Results Observations Human geolocation estimates seem correlated (r = .51) to geolocations estimates from large scale scene matching. For such a difficult task, humans are surprisingly robust to brief viewing durations. Alternatively, humans do not rely on subtle cues (e.g. text, vegetation species) too heavily. The per as long as mance gap as long as l in addition to mark images would likely close with better instance-level recognition methods. The image sets alternate viewing conditions. No learning or fatigue effects were observed. Query Top Matches Geolocation Estimate Chance Chance Chance

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Method Introduction It Pays so that Compare! The Benefits of Contrasting Cases on Students? Learning of Mathematics Jon R. Star1, Bethany Rittle-Johnson2, Kosze Lee3, Jennifer Samson2, in addition to Kuo-Liang Chang3 1Harvard University, 2Vanderbilt University, 3Michigan State University

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Method Introduction It Pays so that Compare! The Benefits of Contrasting Cases on Students? Learning of Mathematics Jon R. Star1, Bethany Rittle-Johnson2, Kosze Lee3, Jennifer Samson2, in addition to Kuo-Liang Chang3 1Harvard University, 2Vanderbilt University, 3Michigan State University

DePauw University, IN has reference to this Academic Journal, It Pays so that Compare! The Benefits of Contrasting Cases on Students? Learning of Mathematics Jon R. Star1, Bethany Rittle-Johnson2, Kosze Lee3, Jennifer Samson2, in addition to Kuo-Liang Chang3 1Harvard University, 2Vanderbilt University, 3Michigan State University Introduction For at least the past 20 years, a central tenet of reform pedagogy in mathematics has been that students benefit from comparing, reflecting on, in addition to discussing multiple solution methods (Silver et al., 2005). Case studies of expert mathematics teachers emphasize the importance of students actively comparing solution methods (e.g., Ball, 1993; Fraivillig, Murphy, & Fuson, 1999). Furthermore, teachers in high-performing countries such as Japan in addition to Hong Kong often have students produce in addition to discuss multiple solution methods (Stigler & Hiebert, 1999). While these in addition to other studies provide evidence that sharing in addition to comparing solution methods is an important feature of expert mathematics teaching, existing studies do not directly link this teaching practice so that measured student outcomes . We could find no studies that assessed the causal influence of comparing contrasting methods on student learning gains in mathematics. There is a robust literature in cognitive science that provides empirical support in consideration of the benefits of comparing contrasting examples in consideration of learning in other domains, mostly in laboratory settings (e.g., Gentner, Loewenstein, & Thompson, 2003; Schwartz & Bransford, 1998). For example, college students who were prompted so that compare two business cases by reflecting on their similarities were much more likely so that transfer the solution strategy so that a new case than were students who read in addition to reflected on the cases independently (Gentner et al., 2003). Thus, identifying similarities in addition to differences in multiple examples may be a critical in addition to fundamental pathway so that flexible, transferable knowledge. However, this research has not been done in mathematics, alongside K-12 students, or in classroom settings. Current Study. We evaluated whether using contrasting cases of solution methods promoted greater learning in two mathematical domains (computational estimation in addition to algebra linear equation solving) than studying these methods in isolation. The research focused on three core learning outcomes: (1) problem-solving skill on both familiar in addition to novel problems, (2) conceptual knowledge of the target domain, in addition to (3) procedural flexibility, which includes the ability so that generate more than one way so that solve a problem in addition to evaluate the relative benefits of different procedures. Algebra equation solving. The transition from arithmetic so that algebra is a notoriously difficult one, in addition to improvements in algebra instruction are greatly needed (Kilpatrick et al., 2001). Algebra historically has represented students? first sustained exposure so that the abstraction in addition to symbolism that makes mathematics powerful (Kieran, 1992). Regrettably, students? difficulties in algebra have been well documented in national in addition to international assessments (Blume & Heckman, 1997; Schmidt et al., 1999). Current mathematics curricula typically focus on standard procedures in consideration of solving equations, rather than on flexible in addition to meaningful solving of equations (Kieran, 1992). In contrast, prompting students so that solve problems in multiple ways leads them so that greater procedural flexibility (Star & Seifert, 2006). Computational Estimation. A large majority of students have difficulty doing simple calculations in their heads or estimating the answers so that problems (e.g., Case & Sowder, 1990; Reys, Bestgen, Rybolt, & Wyatt, 1980). This disuse or inability so that use mental math or estimation is a significant barrier so that using mathematics in everyday life. In addition so that being a fundamental, real-world skill, the ability so that quickly in addition to accurately perform mental computations in addition to estimations has two additional benefits: 1) It allows students so that check the reasonableness of their answers found through other means, in addition to 2) it can help students develop a better understanding of place value, mathematical operations, in addition to general number sense (Kilpatrick et al, 2001). Method We compared learning from studying contrasting cases (compare group) so that learning from studying sequentially presented solutions (sequential, or control, group) in the domains of multi-step linear equations (Study 1; Rittle-Johnson & Star (in press)) in addition to computational estimation (Study 2). Participants, Study 1: Seventy (36 female) 7th graders in addition to their teacher Participants, Study 2: Sixty-nine (32 female) 5th graders in addition to their teacher Procedure: We randomly paired students in addition to assigned them so that condition. Pairs studied worked examples of other students? solutions in addition to answered questions about the solutions during a three-day intervention in their intact math classes. Both conditions were introduced so that the same solution methods in addition to received mini-lectures from the teacher during the intervention. Samples of Intervention Materials Samples of Assessment Items Results 2. Students in the compare condition made greater gains in flexibility. 3. Compare in addition to sequential students achieved similar in addition to modest gains in conceptual knowledge. 1. Students in the compare condition made greater gains in procedural knowledge. Discussion Comparing in addition to contrasting alternative solution methods led so that greater gains in procedural knowledge in addition to flexibility, in addition to comparable gains in conceptual knowledge, compared so that studying multiple methods sequentially. These findings provide direct empirical support in consideration of one common component of reform mathematics teaching. These studies also suggest that prior cognitive science research on comparison as a basic learning mechanism may be generalizable so that new domains (algebra in addition to estimation), a new age group (school-aged children), in addition to a new setting (the classroom). These findings were strengthened by our use of random assignment of students so that condition within their regular classroom context, along alongside maintenance of a fairly typical classroom environment. Further, rather than comparing our intervention so that standard classroom practice, which differs from our intervention on many dimensions, we compared it so that a control condition which was matched on as many dimensions as possible. This allowed us so that evaluate a specific component of effective teaching in addition to learning. The current studies are an important first step in providing experimental evidence in consideration of the benefits of comparing alternative solution methods, but much is yet so that be done. In particular, it is important so that evaluate when in addition to how comparison facilitates learning. We are presently conducting several studies exploring the effectiveness of different types of comparison, including comparing solution strategies (the same problem solved in two different ways), comparing problem types (two different problems, solved using the same strategy), in addition to comparing isomorphs (two similar problems, solved using the same strategy). Our preliminary analyses suggest that the type of comparison that is most effective appears so that depend on prior knowledge in addition to ability. References Ball, D. L. (1993). With an eye on the mathematical horizon: Dilemmas of teaching elementary school mathematics. The Elementary School Journal, 93, 373-397. Blume, G. W., & Heckman, D. S. (1997). What do students know about algebra in addition to functions? In P. A. Kenney & E. A. Silver (Eds.), Results From the Sixth Mathematics Assessment (pp. 225-277). Reston, VA: National Council of Teachers of Mathematics. Case, R., & Sowder, J. T. (1990). The development of computational estimation: A neo-Piagetian analysis. Cognition in addition to Instruction, 7, 79-104. Fraivillig, J. L., Murphy, L. A., & Fuson, K. (1999). Advancing children’s mathematical thinking in Everyday Mathematics classrooms. Journal in consideration of Research in Mathematics Education, 30, 148-170. Gentner, D., Loewenstein, J., & Thompson, L. (2003). Learning in addition to transfer: A general role in consideration of analogical encoding. Journal of Educational Psychology, 95(2), 393-405. Kieran, C. (1992). The learning in addition to teaching of school algebra. In D. Grouws (Ed.), Handbook of Research on Mathematics Teaching in addition to Learning (pp. 390-419). New York: Simon & Schuster. Kilpatrick, J., Swafford, J. O., & Findell, B. (Eds.). (2001). Adding it up: Helping children learn mathematics. Washington DC: National Academy Press. Lindquist, M. M. (Ed.). (1989). Results from the fourth mathematics assessment of the National Assessment of Educational Progress. Reston, VA: National Council of Teachers of Mathematics. Reys, R. W., Bestgen, B., Rybolt, J. F., & Wyatt, J. W. (1980). Identification in addition to characterization of computational estimation processes used by in-school pupils in addition to out-of-school adults (No. ED 197963). Washington, DC: National Institute of Education. Rittle-Johnson, B. & Star, J. (in press). Does comparing solution methods improve conceptual in addition to procedural knowledge? An experimental study on learning so that solve equations. Journal of Educational Psychology. Schmidt, W. H., McKnight, C. C., Cogan, L. S., Jakwerth, P. M., & Houang, R. T. (1999). Facing the consequences: Using TIMMS in consideration of a closer look at U.S. mathematics in addition to science education. Dordrecht: Kluwer. Sowder, J. T., & Wheeler, M. M. (1989). The development of concepts in addition to strategies used in computational estimation. Journal in consideration of Research in Mathematics Education, 20, 130-146. Schwartz, D. L., & Bransford, J. D. (1998). A time in consideration of telling. Cognition in addition to Instruction, 16(4), 475-522. Silver, E. A., Ghousseini, H., Gosen, D., Charalambous, C., & Strawhun, B. (2005). Moving from rhetoric so that praxis: Issues faced by teachers in having students consider multiple solutions in consideration of problems in the mathematics classroom. Journal of Mathematical Behavior, 24, 287-301. Star, J.R., & Seifert, C. (2006). The development of flexibility in equation solving. Contemporary Educational Psychology, 31, 280-300. Stigler, J. W., & Hiebert, J. (1999). The teaching gap: Best ideas from the world’s teachers in consideration of improving education in the classroom. New York: Free Press.

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Informed Choice- Do We Need It? Introduction Law in addition to Ethics Various Approaches Law in addition to Ethics Revisited The Basics The Elements of Choice The Professional-Client Model The Professional-Client Model Difficult Issues Informed Refusal Patient Chooses- Provider Does Not Agree Rules so that Avoid Abandonment Provider-Patient Disagreement The Limits of Autonomy 1 The Limits of Autonomy 2 The Limits of Autonomy 3 The Limits of Autonomy 4 The Limits of Autonomy 5 The Limits of Autonomy 6 The Limits of Autonomy 7 Autonomy-Not a Trump Card ? A Note About Ethics Minors Parental Refusal 1 Parental Refusal 2 Parental Refusal 3 Incompetent Patients Research in addition to Consent Stubborn Patients ?Life is not Worth Living? Patients Testing in consideration of HIV Uniform Anatomical Gift Act Exceptions so that Need in consideration of Consent Documenting the Decision Consent-Do we Need It? Case Study #1 Case Study #2 Case Study #3 Case Study #4 Case Study #5 When Policies Clash Conclusion Remember?

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Protists! What is a Protist? Protists are organisms that are classified int

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Protists! What is a Protist? Protists are organisms that are classified int

DePauw University, US has reference to this Academic Journal, Protists! What is a Protist? Protists are organisms that are classified into the kingdom Protista.ÿ The protists form a group of organisms that really do not fit into any other kingdom.ÿ All protists are eukaryotic.ÿ That is, all protists have cells alongside nuclei.ÿ In addition, all protists live in moist environments. Protists can be unicellular or multicellular.ÿ Protists can be microscopic or can be over 100 meters (300 feet) long.ÿ Some protists are heterotrophs, while others are autotrophs.

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Since protists vary so much, we will group them into three subcategories: animal-like protists, fungus-like protists, in addition to plant-like protists. Animal Like Protists Protists that are classified as animal-like are called protozoans in addition to share some common traits alongside animals.ÿ All animal-like protists are heterotrophs.ÿ Likewise, all animal-like protists are able so that move in their environment.ÿUnlike, animals, however, animal-like protists are all unicellular. Animal-like protists are divided into four basic groups based on how they move in addition to live. PROTISTS WITH PSEUDOPODS: These protists move by extending their bodies forward in addition to then pulling the rest of their bodies forward as well.ÿ The finger-like structures that they project forward are called pseudopods.ÿ The pseudopods are also used so that trap food.

The ameba is an example of this type of animal-like protist. PROTISTS WITH CILIA These protists move by beating tiny hair-like structures called cilia.ÿ The cilia act as tiny oars that allows the protist so that move through its watery environment.ÿ The cilia also help the protists capture food. The paramecium is an example of this type of animal-like protist.

CUDA /OpenCL ? Execution Model From Natural Language so that Electrons The ISA A program at the ISA level The Von-Neumann Model Arrays of Parallel Threads Thread Blocks: Scalable Cooperation blockIdx in addition to threadIdx Vector Addition ? Conceptual View Vector Addition ? Traditional C Code Heterogeneous Computing vecAdd Host Code CUDA Device Memory Management API functions Host-Device Data Transfer API functions Check in consideration of API Errors in Host Code Example: Vector Addition Kernel Example: Vector Addition Kernel More on Kernel Launch Kernel execution in a nutshell Compiling A CUDA Program Questions?

PROTISTS WITH FLAGELLA These protists move by beating their long whiplike structures called flagella.ÿ These protists can have one or more flagella that help them move.ÿ Many of these protists live in the bodies of other organisms. The symbiosis may be mutualistic or parasitic. Giardia: water borne illness FUNGUS LIKE PROTISTS Fungus-like protists are heterotrophs alongside cell walls.ÿ They also reproduce by forming spores.ÿ All fungus-like protists are able so that move at some point in their lives.ÿ There are essentially three types of fungus-like protists:ÿ water molds, downy mildews, in addition to slime molds.

Water molds in addition to Downy mildew Live in water or moist environments Look like tiny threads alongside a fuzzy covering Attack food such as potatoes, cabbage, in addition to corn in addition to can destroy whole crops SLIME MOLDS Live in moist soil in addition to on decaying plants in addition to trees Very colorful Move by forming pseudopods Feed on bacteria in addition to other microorganisms. Plant like Protists Plant-like protists are autotrophic.ÿ They can live in soil, on the bark of trees, in fresh water, in addition to in salt water.ÿ These protists are producers. They produce a lot of oxygen in addition to carbon based organic materials. These plant-like protists form the base of aquatic food chains.

These plant-like protists can be unicellular, multicellular, or live in colonies.ÿ The plant-like protists are divided into four basic groups:ÿ euglenoids, dinoflagellates, diatoms, in addition to algae. ÿ ÿ Euglenoids Autotrophs when sunny Heterotrophs when dark Unicellular Found mostly in fresh water Some have flagellum EUGLENA

Dinoflagellates Autotrophs when sunny Heterotrophs when dark Unicellular Found mostly in fresh water Some have flagellum Dinoflagellates Diatoms Unicellular Glasslike cell walls Used in toothpastes, scouring products, in addition to as filters

Green Algae Are green in color Mostly unicellular, but some form colonies, in addition to a few are multicellular Live in fresh water, salt water, in addition to a few live on land Red Algae Multicellular Commonly called sea weed Live in deep salt water Are used by humans so that help make ice cream in addition to hair conditioner Are eaten in some Asian cultures Brown Algae: ?sea weed? Multicellular Commonly called sea weed Have large leaf-like structures called blades Have air-filled sacs called air bladders Have root-like structure called holdfast Live in salt water Are used by humans so that help make pudding in addition to salad dressing

Brown Algae

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