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Sketching is an excellent brainstorming approach that improves students’ 21st century skills including critical-thinking, communication, and, when working in teams, collaboration. The purpose of the article is to translate the teaching of design sketching, learned through collaborative design & innovation programming at the higher education level, to the K-12 engineering and technology classroom. 

Translation of Design Sketching Practices, Learned Via an Innovation-focused Undergraduate Program, to K-12 Schools. 

Local thermal magnetization fluctuations in Li-doped MnTe are found to increase its thermopower α strongly at temperatures up to 900 K. Below the Néel temperature ( T N ~ 307 K), MnTe is antiferromagnetic, and magnon drag contributes α md to the thermopower, which scales as ~ T 3 . Magnon drag persists into the paramagnetic state up to >3 × T N because of long-lived, short-range antiferromagnet-like fluctuations (paramagnons) shown by neutron spectroscopy to exist in the paramagnetic state. The paramagnon lifetime is longer than the charge carrier–magnon interaction time; its spin-spin spatial correlation length is larger than the free-carrier effective Bohr radius and de Broglie wavelength. Thus, to itinerant carriers, paramagnons look like magnons and give a paramagnon-drag thermopower. This contribution results in an optimally doped material having a thermoelectric figure of merit ZT > 1 at T > ~900 K, the first material with a technologically meaningful thermoelectric energy conversion efficiency from a spin-caloritronic effect. 

Scientific simulations generate large amounts of floating-point data, which are often not very compressible using the traditional reduction schemes, such as deduplication or lossless compression. The emergence of lossy floating-point compression holds promise to satisfy the data reduction demand from HPC applications; however, lossy compression has not been widely adopted in science production. We believe a fundamental reason is that there is a lack of understanding of the benefits, pitfalls, and performance of lossy compression on scientific data. In this paper, we conduct a comprehensive study on state-of- the-art lossy compression, including ZFP, SZ, and ISABELA, using real and representative HPC datasets. Our evaluation reveals the complex interplay between compressor design, data features and compression performance. The impact of reduced accuracy on data analytics is also examined through a case study of fusion blob detection, offering domain scientists with the insights of what to expect from fidelity loss. Furthermore, the trial and error approach to understanding compression performance involves substantial compute and storage overhead. To this end, we propose a sampling based estimation method that extrapolates the reduction ratio from data samples, to guide domain scientists to make more informed data reduction decisions.