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Living cells constantly monitor their external and internal environments for changing conditions, stresses or developmental cues. Networks of genetically encoded components sense and process these signals following pre-defined rules in such a way that specific combinations of the presence or absence of certain signals activate suitable responses. Many biological signal integration mechanisms approximate Boolean logic operations, whereby presence or absence of signals are computed as variables with values described as either true or false, respectively. Boolean logic gates are commonly used in algebra and in computer sciences, and have long been recognized as useful information processing devices in electronic circuits. In these circuits, logic gates integrate multiple input values and produce an output signal according to pre-defined Boolean logic operations. Recent implementation of these logic operations using genetic components to process information in living cells has allowed genetic circuits to enable novel traits with decision-making capabilities. Although several literature reports describe the design and use of these logic gates to introduce new functions in bacterial, yeast and mammalian cells, similar approaches in plants remain scarce, likely due to challenges posed by the complexity of plants and the lack of some technological advances, e.g. , species-independent genetic transformation. In this mini review, we have surveyed recent reports describing synthetic genetic Boolean logic operators in plants and the different gate architectures used. We also briefly discuss the potential of deploying these genetic devices in plants to bring to fruition a new generation of resilient crops and improved biomanufacturing platforms. • Boolean logic operators are commonly used in algebra and computer sciences. • Many biological processes can be modeled as Boolean logic operations. • Genetically encoded Boolean logic operators are powerful tools in synthetic biology. • Only recently Boolean logic has been applied to plant synthetic biology. • Different genetic parts and topologies can be combined to build logic operators. [ABSTRACT FROM AUTHOR]

The Radicalism of Ethnomethodology: An Assessment of Sources and Principles By Martyn Hammersley (Manchester University Press, 2018) The structure of this book is a preface, an introduction, five main chapters, and then a conclusion. This use of non-ironic theoretical "ironicization", the avoidance of which is central to ethnomethodology's critiques and disavowal of formal sociology, is one of the ways Hammersley displays he really has not grasped ethnomethodology. This really becomes clear in the following chapter in I his i account of ethnomethodology's presuppositions which displays classic formal social theorization on Hammersley's part, by outlining sociological theories prior to ethnomethodology, projecting them into ethnomethodology, then making ethnomethodology - or his interpretation of who counts as an ethnomethodologist, and their writings - accountable to those theories, whether they acknowledged themselves as accountable to them or not. [Extracted from the article]

Since high performance is essential to the functioning of emergency departments (EDs), they must constantly pursue sensible and empirically testable improvements. In light of recent advances in computer science, an increasing number of simulation-based approaches for studying and implementing ED performance optimizations have become available in the literature. This paper aims to offer a survey of these works, presenting progress made on the topic while indicating possible pitfalls and difficulties in EDs. With that in mind, this review considers research studies reporting simulation-based optimization experiments published between 2007 and 2019, covering 38 studies. This paper provides bibliographic background on issues covered, generates statistics on methods and tools applied, and indicates major trends in the field of simulation-based optimization. This review contributes to the state of the art on ED modeling by offering an updated picture of the present state of the field, as well as promising research gaps. In general, this review argues that future studies should focus on increasing the efficiency of multi-objective optimization problems by decreasing their cost in time and labor. [ABSTRACT FROM AUTHOR]

Element-free Galerkin (EFG) method was formulated to simulate elastic fracture in structural mechanics and it possesses great advantages by allowing more flexible numerical treatment on moving interface without mesh regeneration. It has become the research interest of scientific computing to transplant the EFG algorithm into Computational Fluid Dynamics (CFD) whenever deforming boundaries set in. However, compared with Arbitrary-Lagrangian-Eulerian method and Immersed Boundary method, the development and application of EFG method in CFD is still in infancy. The goal of the review paper is to systematically bridge the current development of EFG methods with its future applications in CFD. [ABSTRACT FROM AUTHOR]

Scientific discovery increasingly relies on computation through simulations, analytics, and machine and deep learning. Of these, simulations on high-performance computing (HPC) platforms have been the cornerstone of scientific computing for more than two decades. However, the development of simulation software has, in general, occurred through accretion, with a few exceptions. With an increase in scientific understanding, models have become more complex, rendering an accretion mode untenable to the point where software productivity and sustainability have become active concerns in scientific computing. In this survey paper, we examine a modest set of HPC scientific simulation applications that are already using cutting-edge HPC platforms. Several have been in existence for a decade or more. Our objective in this survey is twofold: first, to understand the landscape of scientific computing on HPC platforms in order to distill the currently scattered knowledge about software practices that have helped both developer and software productivity, and second, to understand the kind of tools and methodologies that need attention for continued productivity. [ABSTRACT FROM AUTHOR]

For this reason and the new computing skills I learned while reading it, my personal copy of I Computing Skills for Biologists: A Toolbox i has numerous dog-eared pages. Allesina and Wilmes introduce the Unix operating system, several programming languages (R, python, and SQL), and useful tools such as Git and LaTeX. As such, I Computing Skills for Biologists: A Toolbox i is a must read for the next generation of biologists. [Extracted from the article]

Data is the prime mover of computational analysis and the central actor in Machine Learning by Ethem Alpaydin, a 2016 book in the MIT Press Essential Knowledge Series. [ABSTRACT FROM PUBLISHER]

Michael Jay Schillaci reviews “Computational Modeling and Visualization of Physical Systems with Python” by Jay Wang, declaring it deserving of becoming a standard in undergraduate and graduate curricula where scientific computing plays a role. [ABSTRACT FROM PUBLISHER]

Kevin Thielen and Vivienne Tien review J.M. Kinder and P. Nelson's "A Student's Guide to Python for Physical Modeling", declaring that readers can expect to not only know how to write and read Python but also to develop a thorough understanding for developing complex physical models and calculations. [ABSTRACT FROM PUBLISHER]

This book differentiates itself through a number of seemingly small design decisions that are powerful and go a long way in realizing an effective and instructive textbook for a first course in the subject. Students in the second year of an engineering program, especially those who have gone through the standard mathematics core courses, will also find themselves comfortable in studying and learning from this book. The book?s organization is straightforward but effective. After two introductory chapters dedicated to signals, systems,and the mathematical background required to study them, the text continues with two sets of chapters focusing, respectively, on analog and digital signals and systems. The two sets are organized very similarly, sequentially dedicating one chapter each to signals in the time domain, systems in the time domain, signals in the frequency domain, and systems in the frequency domain. The time domain chapters mostly examine classification, with the addition of sampling operation when discussing digital signals. The frequency domain chapters consider the different fundamental transforms (such as the continuous- and discrete-time Fourier transform, the Laplace transform for analog signals and systems, and the Z-transform for digital signals and systems) and the techniques for studying system response to different input signals. The last two chapters are dedicated to the discrete Fourier transform, including its relation to the discrete-time Fourier transform and the fast Fourier transform algorithm itself. The book distinguishes itself from others in the same category through several design decisions that go a long way in helping the reader learn. [ABSTRACT FROM PUBLISHER]

Blaine Willhoft and Rob Willhoft review "Software Design Decoded: 66 Ways Experts Think" by Marian Petre and André van der Hoek, a collection of short, one-paragraph observations on the habits that successful engineers have developed or learned over time. [ABSTRACT FROM PUBLISHER]

Carl Kingsford reviews R. Libeskind-Hadas and E. Bush's Computing for Biologists: Python Programming and Principles, declaring it good at avoiding intimidating students with unnecessary complexity while still providing a respectable programming and algorithmic introduction with a strong biology motivation. [ABSTRACT FROM PUBLISHER]

Kalyani Nair reviews "Multiscale Modeling in Biomechanics and Mechanobiology", edited by S. De, W. Hwang, and E. Kuhl, declaring it useful for anyone looking to get a quick overview of the field over a broad spectrum of areas. [ABSTRACT FROM AUTHOR]

Daniel Teske reviews the, "Introduction to Computational Science: Modeling and Simulation for the Sciences, 2nd ed. (Shiflet, A.B. and Shiflet, G.W.; 2014). This second edition covers agent-based simulations and modeling with matrices in addition to the three methods covered in the first edition: system dynamics models, empirical modeling, and cellular automaton simulations. The agent-based models are an impressive addition, with user-friendly choices for the necessary software. The text uses a pseudocode approach, offering several possible software choices for each of the five modeling methods. It?s possible for an instructor to cover the entire text using open source software supplemented with the authors? own software-specific tutorials. For example, if you use R for matrix models, an extensive set of tutorials on R can be downloaded from the text?s website. The coverage spans the hard sciences, with applications and a large number of programming projects from physics, engineering, chemistry, biochemistry, and biology. Most of the models cover biological systems, and the text does a good job introducing biology students to scientific computing. Students with neither programming nor modeling experience are likely to be challenged, but with some care given to pace and topic selection, biology students with no prior programming experience could learn to numerically model some complex biological systems. [ABSTRACT FROM PUBLISHER]

Here, Jeffrey Shallit reviews the book It Began with Babbage: The Genesis of Computer Science by Subrata Dasgupta, assessing its contents, organization, and the audience for which it's suitable. [ABSTRACT FROM AUTHOR]

Here, Prodyot K. Basu reviews the book The Finite Element Method by Mats G. Larson and Fredrik Bengzon, assessing its contents, organization, and the audience for which it's suitable. [ABSTRACT FROM PUBLISHER]

This book should be seriously considered as a textbook by anyone teaching computational physics to upper-division physics students. It would be one of my top choices to teach an upper-division computational physics class. The book includes good coverage of the standard techniques usually covered in a numerical methods course: ordinary differential equations, root finding, partial differential equations, integration, Fourier transforms, linear algebra, and minimization with the addition of a few topics of special interest to physicists?chaos, neural networks, and Galerkin methods. Almost all of the techniques are couched in the context of physics examples from the areas of relativistic and classical mechanics, electricity and magnetism, quantum mechanics, thermodynamics, and fluid dynamics. [ABSTRACT FROM PUBLISHER]

Wesley Snyder reviews a book, "Computer Vision, Models, Learning, and Inference," that uses a theme-based (rather than a technique-based) approach to teach the basics of computer vision. [ABSTRACT FROM PUBLISHER]

A review of Karline Soetaert, Jeff Cash, and Francesca Mazzia's book, "Solving Differential Equations in R". [ABSTRACT FROM PUBLISHER]

The author reviews the book “Modern Fortran: Style and Usage”, explaining why it's a good reference guide not only for novice Fortran users, but for anyone who wants to improve their code for clarity and performance. [ABSTRACT FROM AUTHOR]

Reviews the book "Computer Applications in the Earth Sciences -- Update of the 70s," by D. F. Merriam.

The article reviews the book "Practical Computing for Biologists," by Steven H. D. Haddock and Casey W. Dunn.

The author reviews a book dealing with the intricacies associated with Matlab (a popular platform for numerical computing) and its Java interface. [ABSTRACT FROM PUBLISHER]

This column provides a review of an introductory book on the use of Python for scientific programming. [ABSTRACT FROM PUBLISHER]

This issues reviews include a book on object-oriented design of scientific software that explores useful design patterns for scientific computation and an algorithm primer for nanoscience. [ABSTRACT FROM PUBLISHER]

In this issue's column, the reviewed books offering guidelines for using Matlab and an introduction to graphics processing units. [ABSTRACT FROM PUBLISHER]

The latest in a series of books on "GPU gems," this volume offers a collection of relatively short reports on various GPU applications. [ABSTRACT FROM PUBLISHER]

Featured reviews include a highly readable new book on parallel programming in Java and an introduction to biophysics to help science and engineering students thrive in the "century of biology." [ABSTRACT FROM PUBLISHER]

Numerical Relativity is a useful textbook that will allow smart graduate students to carry out a wide swath of research in an important subject. [ABSTRACT FROM PUBLISHER]

This well-organized volume shows scientists and engineers how to improve their computing performance using multiple concurrency and data locality. [ABSTRACT FROM PUBLISHER]

This book gives newcomers an entry point for using density functional theory to accurately predict materials properties on a computer. [ABSTRACT FROM PUBLISHER]

Stephen M. Barnett's Quantum Information offers a strong, broadly accessible introductory guide to the field of quantum information science. In Scientific Computation, Gaston Gonnet and Ralf Scholl treat a range of algorithms and offer a unique motivating problem class, encouraging readers to rethink scientific computation's value. [ABSTRACT FROM PUBLISHER]