Molecular Polarity.ppt
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Molecular polarity depends on two factors: the type of bonds in a molecule and the arrangement of those bonds. For diatomic molecules, bond polarity and molecular polarity are the same. However, for larger molecules the arrangement of bonds must be considered. A molecule may contain polar bonds but not be polar itself if it is symmetric, as the "pull" from one polar bond cancels out the "pull" from another. Symmetry can be determined by the presence of at least one mirror plane. Nonpolar molecules like CO2 are symmetric with polar bonds that cancel out, while polar molecules like H2O are asymmetric with polar bonds that do not cancel out.
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Ionic compounds are formed between a metal and nonmetal where electrons are transferred, creating oppositely charged ions held together by ionic bonds. Covalent molecular compounds are formed between two or more nonmetals where atoms share electrons through covalent bonds to form electrically neutral molecules like table sugar. Both compound types exhibit distinct physical properties depending on their bonding such as ionic compounds having high melting points and being brittle while covalent compounds have low melting points and are soft and flexible.
Chapter07 130905235032-
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The Chemistry of Life Mid-Unit Test Review
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
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With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
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Slides from talk:
Aleš Zamuda: Randomised Optimisation Algorithms in DAPHNE .
Austrian-Slovenian HPC Meeting 2024 – ASHPC24, Seeblickhotel Grundlsee in Austria, 10–13 June 2024
https://ashpc.eu/
Deep Software Variability and Frictionless Reproducibility
Deep Software Variability and Frictionless ReproducibilityUniversity of Rennes, INSA Rennes, Inria/IRISA, CNRS
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
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Molecular Polarity.ppt
- 2. Molecular Polarity • Depends on 2 factors. –Type of bonds in the molecule –Arrangement of bonds or shape of molecule
- 3. Diatomic Molecules • Simplest case: • Bond Polarity and Molecular Polarity are identical • Larger Molecules – Have to look at how the bonds are arranged.
- 4. For larger molecules, look at the kind & arrangement of bonds to determine overall polarity of molecule. CH4 has 4 bonds.
- 5. Polarity of Molecules • A molecule may contain polar bonds, but not be polar! Depends on the geometry of the molecule. • If molecule is symmetric, the “pull” of one polar bond is offset by the “pull” of another polar bond. – It’s a tug-of-war that no one can win!
- 6. Symmetric Molecules • Contain at least one mirror plane.
- 7. Symmetric vs. Asymmetric • CO2 is nonpolar. • Can’t tell the ends apart. • H2O is bent. • Electron cloud is lopsided. • H2O is polar.
- 8. Polarity of CO2? Draw an arrow along each bond pointing to the more electronegative atom. If the arrows cancel out, the molecule is NONPOLAR.
- 9. Polarity of H2O? The green arrows do not cancel out, so water is polar!
- 10. Symmetry of Larger Molecules CF4 is fairly symmetric. Overall, it is nonpolar. The arrows cancel out.
- 11. Use the structural formula to predict Molecular Polarity! H H C H H
- 12. Ethane = C2H6 Ethene = C2H4 Ethyne = C2H2 These molecules are symmetric and the electron cloud is the same on both ends. Overall, they are nonpolar.
- 13. Molecular Polarity • If you know the shape, you can use the arrow technique to determine the polarity. • So how do you get the shape?
- 14. Polarity – Linear molecules • Nonpolar if the ends are the same • Polar if the ends are different – Tetrahedral • Nonpolar if all 4 corners are the same • Polar if even 1 corner is different – Trigonal Pyramids • Always polar – Bent molecules • Always polar