A common feature of the recent calls for reform of the undergraduate biology curriculum has been for better coordination between biology and the courses from the allied disciplines of mathematics, chemistry, and physics. Physics has lagged behind math and chemistry in creating new, biologically oriented curricula, although much activity is now taking place, and significant progress is being made. In this essay, we consider a case study: a multiyear conversation between a physicist interested in adapting his physics course for biologists (E.F.R.) and a biologist interested in including more physics in his biology course (T.J.C.). These extended discussions have led us both to a deeper understanding of each other's discipline and to significant changes in the way we each think about and present our classes. We discuss two examples in detail: the creation of a physics problem on fluid flow for a biology class and the creation of a biologically authentic physics problem on scaling and dimensional analysis. In each case, we see differences in how the two disciplines frame and see value in the tasks. We conclude with some generalizations about how biology and physics look at the world differently that help us navigate the minefield of counterproductive stereotypical responses.
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PLoS One
January 2025
Faculty of Education and Arts, Sohar University, Sohar, Oman.
Conjugate Gradient (CG) methods are widely used for solving large-scale nonlinear systems of equations arising in various real-life applications due to their efficiency in employing vector operations. However, the global convergence analysis of CG methods remains a significant challenge. In response, this study proposes scaled versions of CG parameters based on the renowned Barzilai-Borwein approach for solving convex-constrained monotone nonlinear equations.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Laboratory for Atomistic and Molecular Mechanics, Massachusetts Institute of Technology, Cambridge, MA 02139.
The design of new alloys is a multiscale problem that requires a holistic approach that involves retrieving relevant knowledge, applying advanced computational methods, conducting experimental validations, and analyzing the results, a process that is typically slow and reserved for human experts. Machine learning can help accelerate this process, for instance, through the use of deep surrogate models that connect structural and chemical features to material properties, or vice versa. However, existing data-driven models often target specific material objectives, offering limited flexibility to integrate out-of-domain knowledge and cannot adapt to new, unforeseen challenges.
View Article and Find Full Text PDFRapid Commun Mass Spectrom
January 2025
Project Center of Advanced Mass Spectrometry Technologies, Skolkovo Institute of Science and Technology, Moscow, Russian Federation.
Rationale: Teaching mass spectrometry essentials is usually connected with one of the basic courses for undergrads. Thus, specific previous knowledge is required from students. However, the necessity of teaching mass spectrometry essentials to students of different academic specializations and multidisciplinary groups can arise in every academic group.
View Article and Find Full Text PDFMed Phys
January 2025
Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Kraków, Poland.
Background: Total-body (TB) Positron Emission Tomography (PET) is one of the most promising medical diagnostics modalities, opening new perspectives for personalized medicine, low-dose imaging, multi-organ dynamic imaging or kinetic modeling. The high sensitivity provided by total-body technology can be advantageous for novel tomography methods like positronium imaging, demanding the registration of triple coincidences. Currently, state-of-the-art PET scanners use inorganic scintillators.
View Article and Find Full Text PDFBiol Cybern
January 2025
Institute for Physics and Astronomy, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476, Potsdam, Germany.
Piecewise-deterministic Markov processes combine continuous in time dynamics with jump events, the rates of which generally depend on the continuous variables and thus are not constants. This leads to a problem in a Monte-Carlo simulation of such a system, where, at each step, one must find the time instant of the next event. The latter is determined by an integral equation and usually is rather slow in numerical implementation.
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