Chemistry Working Model

[Mariela Laflam]

Thegoal of the Chemistry Climate Working Group (CCWG) is to continue to develop the representation of chemical gas and aerosol species in the CESM with the aim to improve our knowledge of the interactions between chemistry, aerosols and climate. CCWG science topics include past, present and future atmospheric composition in the troposphere and lower- to mid- stratosphere; interactions between atmospheric composition and other components of the Earth System (including land, ocean and ice); stratosphere-troposphere coupling; aerosol direct and indirect effects on the Earth's energy balance; aerosol effects on precipitation; and the impacts of chemistry and aerosol on both climate and air quality across multiple spatiotemporal scales.

CESM2 CAM-chem will serve as the new workhorse model to perform fully-coupled experiments for CMIP7, including comprehensive tropospheric and stratospheric chemistry (MOZART TS1). CAM-chem will provide the aerosol and chemistry fields to be prescribed in CAM7 with simple chemistry for longer climate-scale simulations. The new model lid is at 80 km, and comprises 93 vertical layers with a default horizontal resolution of about 1 degree on a cubed sphere, run on a spectral element dynamical core.

CAM-chem can be run as a free-running climate model, coupled to the land model (CLM), and optionally to the ocean and ice models. It can also be nudged to winds and temperatures of meteorological reanalyses, with a default of using MERRA2 and GEOS5 fields, for detailed comparisons to field experiments and specific observations.

Upcoming developments will prioritize evaluation and improvement of CAM7-chem. In addition, configurations of CAM-chem will be developed that support regionally refined model grids and data assimilation for regional impact studies (air quality and climate). Our long-term goal is to facilitate greater flexibility for users to select different options of chemistry and aerosols schemes, in collaboration with the MUSICA and SIMA frameworks.

The Chemistry Climate Working Group welcomes new users of CESM2 and CAM-chem (Low 40 km and Mid-Top 80 km) and WACCM (High-Top 140 km). You can find more information about WACCM from the Whole Atmosphere Working Group. To help new users get started, we have a wiki page along with helpful guides. For more information about CESM2 with chemistry see the links below:

Support for running CESM is available on the DiscussCESM bulletin board, where users can request assistance with science and runtime issues. The bulletin board includes a CAM-chem support forum section as well as a WACCM support forum section.

Recently, Daniel Cook aka Danc from Lost Garden wrote another essay on Gamasutra. It is very interesting. Danc compares Game Design to alchemy and urges to develop a more systematic approach akin to chemistry. I certainly agree that we need to work on a theory of game design. However I am a bit concerned that the model he proposes might have some flaws. Here is a list of comments on his essay:

What is it good for?

A way to improve the theory might be to start thinking about why we are doing it. How would this kind of Skill Chain Diagram fit into a game design process? What kind of insights might game designers draw out of it. How do we recognize problems? How does an ideal Skill Chain Diagram look like? Danc already mentioned what impact burnout can have. Is this the purpose of the Skill Chain Diagram? To check against reality and cross out the skills which have been missed because of burnout? Would it be impossible to recognize those faults without such diagram? In the end, Alexey Pajitnov made Tetris without it. How could the Diagram be improved to pragmatically augment the Game Design process?

Of course in the end, we live in exciting times and it is extremely motivating to see other people like Danc also work towards a better understanding of the medium. I certainly do hope that we can start a discussion to gradually improve and diversify our theories, models and tools.

Actually, it is proven. What Cook describes is amazingly similar to basic learning theory. His chain of skill atoms is like a rubric for a lesson plan. Educators use this kind of model all the time to teach. A good game is basically a good teacher. jolifanta.wordpress.com

What is it good for?

There are two uses of skill atoms.

- Clarifying a design: This is a nice simple framework with very few exceptions. By mapping your design onto this framework, you are forced to be explicit about things that would otherwise involve hand waving. I find that the simple act of writing something down can help clarify my thought process. There are lots of similar techniques. Skill chains tends to be a bit more comprehensive than most since they clearly include all elements in a functional framework. When you add that new piece of art, you can clearly assign it a purpose. Admittedly, some folks enjoy their fuzzy thinking and random urges to add random things to their random project. In a strongly artist medium like game design, not all creators are interested in analytic thought.

It is this second use where I see the most promise for skill chains. Game design is a highly iterative activity. If we can give game developers easy-to-implement feedback mechanisms that let them understand where they fail in a rapid clear-cut manner, they can evolve their games towards a fun state more quickly.

ChronoDK: I think I need to apologize a bit. Of course, my intension was not to argue against scientific thinking. In the contrary: science means developing falsifiable theories. So the way to do science is to be as skeptical as possible. This can be easily misunderstood, especially if the skeptic is not diplomatic enough.

Skill atoms ask some really basic questions:

- When do players start performing important actions in the game?

- When does the player experience designer-specified feedback intended to cue them into learning a new set of actions?

- When do players stop performing interesting actions?

These are very measurable activities that can be logged in the game, not pie in the sky theory. The theory, however, suggests what to measure and why it is important. One without the other is flying blind.

Instead, you accumulate skill atoms through iterative building, testing and watching players interact with a system. With the complex simulations (even Tetris!) at the heart of most games, it is difficult to predict what skills are valuable until you play. The good designer observes what behaviors are interesting and then codifies them with feedback systems so that they are accessible by and interesting to a broader population. Actions + Simulation + Feedback. In other words, they create skill atoms.

And then you test your design. Did people learn the skill? Did they use it? For how long? Watch them, did they become frustrated or bored when the logging system said that they had stopped pursuing a skill? At this point, you revise your atoms. Ideally the design improves.

Creating working models for Class 12 science can be a fascinating way to understand and apply various scientific principles. Here are 15 ideas spanning physics, chemistry, and biology: Physics Chemistry Biology @howtofunda

The site is secure.

The ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

alpha(S2)-Casein (alpha(S2)-CN) comprises up to 10% of the casein fraction in bovine milk. The role of alpha(S2)-CN in casein micelles has not been studied in detail in part because of a lack of structural information on the molecule. Interest in the utilization of this molecule in dairy products and nutrition has been renewed by work in 3 areas: biological activity via potentially biologically active peptides, functionality in cheeses and products, and nutrition in terms of calcium uptake. To help clarify the behavior of alpha(S2)-CN in its structure-function relationships in milk and its possible applications in dairy products, this paper reviews the chemistry of the protein and presents a working 3-dimensional molecular model for this casein. The model was produced by threading the backbone sequence of the protein onto a homologous protein: chloride intracellular channel protein-4. Overall, the model is in good agreement with experimental data for the protein, although the amount of helix may be over-predicted. The model, however, offers a unique view of the highly positive C-terminal portion of the molecule as a surface-accessible area. This region may be the site for interactions with kappa-carrageenan, phosphate, and other anions. In addition, most of the physiologically active peptides isolated from alpha(S2)-CN occur in this region. This structure should be viewed as a working model that can be changed as more precise experimental data are obtained.

Although Chemical Engineering has existed for only 100 years, its name is no longer completely descriptive of this dynamic profession. The work of the chemical engineer is not restricted to the chemical industry, chemical changes, or chemistry. Instead, modern chemical engineers are concerned with all the physical, chemical, and biological changes of matter that can produce an economic product or result that is useful to humankind.

The education of the chemical engineer is based on the fundamental sciences of physics, chemistry and biology, on mathematical and computer techniques, and on basic engineering principles. This background makes the chemical engineer extremely versatile and capable of working in a variety of industries: chemical, biochemical, petroleum, materials, microelectronics, environmental, food processing, consumer products, consulting and project management. It is also good preparation for law and medical schools.

Successful applicants must have earned a minimum 2.5 grade point average in the better of two attempts of the eight preprofessional courses and a minimum 2.5 grade point average in the better of two attempts of the preprofessional calculus course sequence.

3a8082e126