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Machine learning (ML) has shown to be an effective alternative to physical models for quality prediction and process optimization of metal additive manufacturing (AM). However, the inherent “black box” nature of ML techniques such as those represented by artificial neural networks has often presented a challenge to interpret ML outcomes in the framework of the complex thermodynamics that govern AM. While the practical benefits of ML provide an adequate justification, its utility as a reliable modeling tool is ultimately reliant on assured consistency with physical principles and model transparency. To facilitate the fundamental needs, physics-informed machine learning (PIML) has emerged as a hybrid machine learning paradigm that imbues ML models with physical domain knowledge such as thermomechanical laws and constraints. The distinguishing feature of PIML is the synergistic integration of data-driven methods that reflect system dynamics in real-time with the governing physics underlying AM. In this paper, the current state-of-the-art in metal AM is reviewed and opportunities for a paradigm shift to PIML are discussed, thereby identifying relevant future research directions. 

Key challenges to regionalization of methane fluxes in the Amazon basin are the large seasonal variation in inundated areas and habitats, the wide variety of aquatic ecosystems throughout the Amazon basin, and the variability in methane fluxes in time and space. Based on available measurements of methane emission and areal extent, seven types of aquatic systems are considered: streams and rivers, lakes, seasonally flooded forests, seasonally flooded savannas and other interfluvial wetlands, herbaceous plants on riverine floodplains, peatlands, and hydroelectric reservoirs. We evaluate the adequacy of sampling and of field methods plus atmospheric measurements, as applied to the Amazon basin, summarize published fluxes and regional estimates using bottom-up and top-down approaches, and discuss current understanding of biogeochemical and physical processes in Amazon aquatic environments and their incorporation into mechanistic and statistical models. Recommendations for further study in the Amazon basin and elsewhere include application of new remote sensing techniques, increased sampling frequency and duration, experimental studies to improve understanding of biogeochemical and physical processes, and development of models appropriate for hydrological and ecological conditions. 

In this article the recent developments of the open-source OpenMolcas chemistry software environment, since spring 2020, are described, with the main focus on novel functionalities that are accessible in the stable branch of the package and/or via interfaces with other packages. These community developments span a wide range of topics in computational chemistry, and are presented in thematic sections associated with electronic structure theory, electronic spectroscopy simulations, analytic gradients and molecular structure optimizations, ab initio molecular dynamics, and other new features. This report represents a useful summary of these developments, and it offers a solid overview of the chemical phenomena and processes that OpenMolcas can address, while showing that OpenMolcas is an attractive platform for state-of-the-art atomistic computer simulations. 

We define big data as large amounts of information, collected about many people, over multiple devices. We define critical big data research as efforts to demonstrate how flaws — ethical or methodological — in the collection and use and of big have implications for social inequality. There are many critical and creative big data research endeavors around the world. Here we present an annotated catalog of projects that: are both critical and creative in their analysis of big data; have a distinct Principal Investigator (PI) or clear team; and, are producing an identifiable body of public essays, original research, or civic engagement projects. We have catalogued these endeavors with as much descriptive information as possible, and organized projects by the domains of big data critique and creativity in which they are having an impact. We identify some 35 distinct projects, and several dozen individual researchers, artists and civic leaders, operating in 16 domains of inquiry. We recommend expanding critical and creative work in several domains: expanding work in China; supporting policy initiatives in Latin America’s young democracies; expanding work on algorithmic manipulation originating in authoritarian countries; identifying best practices for how public agencies in the United States should develop big data initiatives. We recommend that the next stage of support for these lines of inquiry is to help publicize the output of these projects, many of which are of interest to a handful of specialists but should be made accessible to policy makers, journalists, and the interested public.