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Undergraduate and graduate students need professional development skills to form expertise applicable to any job or future career. Mentoring is a way that students can learn how to engage in professional development. Likewise, students can learn professional development skills from mentors who they look to for expanding their knowledge base. To help address the needs of undergraduate and graduate students in engineering, the principal investigator developed and facilitated the Mentoring and Professional Development in Engineering Education (MPD-E2) Program. For this study, we examined the program’s general functions and elements using session notes and discussion of our observations. The guiding research question for this study is: what are some elements of a mentoring and professional development program that students value? In this work, we present details about the elements of the program that support student development and insights about potential future opportunities for these types of programs. 

Abstract Current U.S. policies aim to establish domestic supply chains of critical minerals for the energy transition. The Iron Creek deposit in the Idaho cobalt belt (ICB) is one of the most promising cobalt (Co) targets. Our case study illustrates the importance of mineralogy in strategic evaluations of critical mineral potential. Most of the Co at Iron Creek occurs as Fe substitution in pyrite, with lattice-bound and inclusion-hosted Ag, As, Bi, Ni, Pb, Se, Te ± trace Au and Sb. Cobalt also occurs in minor cattierite-vaesite. The Co minerals are intergrown with Co-poor chalcopyrite hosting Cu ± minor In and Zn. Worldwide, most Co is recovered from deposits mineralogically distinct from the ICB, and the United States currently lacks infrastructure to recover this Co and its associated metals. ICB ore minerals could be processed by autoclave, roaster, smelter, bioleach, or heap leach. Recovery of the Ag, As, Au, Bi, In, Pb, Se, Te, and Zn would be costly by autoclave, and construction of a custom smelter for ICB ores is likely uneconomic, so these elements would become waste irrespective of criticality. The Co-Fe and Co-As sulfide minerals are most suitable for Co and Ni recovery by a hydrometallurgical autoclave process, with potential pretreatment of cobaltiferous pyrite/arsenopyrite in an inert-atmosphere roaster, in new domestic or anticipated international facilities. The ICB is the second largest known Co resource in the United States. Consideration of ore mineralogy in the ICB is essential in strategies for domestic production. 

Advances in immersive virtual reality (IVR) are creating more computer-supported collaborative learning environments, but there is little research explicating how collaboration in IVR impacts learning. We ran a quasi-experimental study with 80 participants targeting ocean literacy learning, varying the manner in which participants interacted in IVR to investigate how the design of collaborative IVR experiences influences learning. Results are discussed through the lens of collaborative cognitive learning theory. Participants that collaborated to actively build a new environment in IVR scored higher for learning than participants who only watched an instructional guide’s avatar, or participants who watched the guide’s avatar and subsequently discussed what they learned while in IVR. Moreover, feeling negative emotions, feeling active in the environment, and feeling bonded to the group members negatively correlated with learning. Results shed light on the mechanisms behind how collaborative tasks in IVR can support learning. 

This commentary discusses new advances in the predictability of east African rains and highlights the potential for improved early warning systems (EWS), humanitarian relief efforts, and agricultural decision‐making. Following an unprecedented sequence of five droughts, 23 million east Africans faced starvation in 2022, requiring >$2 billion in aid. Here, we update climate attribution studies showing that these droughts resulted from an interaction of climate change and La Niña. Then we describe, for the first time, how attribution‐based insights can be combined with the latest dynamical models to predict droughts at 8‐month lead‐times. We then discuss behavioral and social barriers to forecast use, and review literature examining how EWS might (or might not) enhance agro‐pastoral advisories and humanitarian interventions. Finally, in reference to the new World Meteorological Organization “Early Warning for All” Executive Action Plan, we conclude with a set of recommendations supporting actionable and authoritative climate services.Trust,urgency, andaccuracycan help overcome barriers created bylimitedfunding,uncertain tradeoffs, andinertia. Understanding how climate change is producing predictable climate extremes now, investing in African‐led EWS, and building better links between EWS and agricultural development efforts can support long‐term adaptation, reducing chronic needs for billions of dollars in reactive assistance. In Africa and beyond, climate change brings increasingly extreme sea surface temperature (SST) gradients. Using climate models, we can often see these extremes coming. Prediction, therefore, offers opportunities for proactive risk management and improved advisory services, if we can create effective societal linkages via cross‐silo collaborations.

 

The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15.5B parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention. StarCoderBase is trained on 1 trillion tokens sourced from The Stack, a large collection of permissively licensed GitHub repositories with inspection tools and an opt-out process. We fine-tuned StarCoderBase on 35B Python tokens, resulting in the creation of StarCoder. We perform the most comprehensive evaluation of Code LLMs to date and show that StarCoderBase outperforms every open Code LLM that supports multiple programming languages and matches or outperforms the OpenAI code-cushman-001 model. Furthermore, StarCoder outperforms every model that is fine-tuned on Python, can be prompted to achieve 40% pass@1 on HumanEval, and still retains its performance on other programming languages. We take several important steps towards a safe open-access model release, including an improved PII redaction pipeline and a novel attribution tracing tool, and make the StarCoder models publicly available under a more commercially viable version of the Open Responsible AI Model license. 

Abstract: Solid-state ion conduction (SSIC) is a mechanism of ionic current that has garnered increasing attention for applications in all-solid-state batteries and atomic switches. The Ag/S SSIC system in β-Ag S, possessing the highest ionic conductivity of any known material, provides a unique opportunity to better understand the fundamental nature of SSIC. β-Ag S is topographically similar to binary perovskites except that it is cubic, leading to isotropic SSIC exceeding 4 S/cm. The dynamic nature of SSIC makes it difficult to study by observational means, where inherent time-averaging obscures correlations among atomic transit routes.Molecular dynamics (MD) is a tool ideally suited for gaining insight into large atomic systems with subnanosecond time resolutions. However, traditional MD potentials lack a description of bond-breaking/forming reactions, which are an essential aspect of SSIC and related memristic properties. This limitation can be overcome by using a reactive force field (ReaxFF), which enables the simulation of bonding reactions with DFT-level accuracy. In this study, we present a ReaxFF force field for the Ag/S system, optimized for simulating SSIC in β-Ag S. Training data consisted of crystal structures, Bader partial charges, and energies of various Ag/S clusters calculated at the DFT-level. Energies were obtained with Gaussian 16, using the PBEh1PBE hybrid functional with a triple-zeta correlation-consistent basis set. Multiobjective parameter optimization was accomplished with an updated form of the Genetic Algorithm for Reactive Force Fields (GARFfield). The force field was validated with potential energy and ion conductivity calculations, along with relevant structural features. Results were compared with equivalent simulations from other established potentials. This new ReaxFF force field will enable modeling of realistic SSIC configurations for Ag/S-based materials and provides a viable approach for extending ReaxFF to other SSIC systems in the future. This work was supported by the National Science Foundation under grant #2025319. 

The Observing Air–Sea Interactions Strategy (OASIS) is a new United Nations Decade of Ocean Science for Sustainable Development programme working to develop a practical, integrated approach for observing air–sea interactions globally for improved Earth system (including ecosystem) forecasts, CO2 uptake assessments called for by the Paris Agreement, and invaluable surface ocean information for decision makers. Our “Theory of Change” relies upon leveraged multi-disciplinary activities, partnerships, and capacity strengthening. Recommendations from >40 OceanObs’19 community papers and a series of workshops have been consolidated into three interlinked Grand Ideas for creating #1: a globally distributed network of mobile air–sea observing platforms built around an expanded array of long-term time-series stations; #2: a satellite network, with high spatial and temporal resolution, optimized for measuring air–sea fluxes; and #3: improved representation of air–sea coupling in a hierarchy of Earth system models. OASIS activities are organized across five Theme Teams: (1) Observing Network Design & Model Improvement; (2) Partnership & Capacity Strengthening; (3) UN Decade OASIS Actions; (4) Best Practices & Interoperability Experiments; and (5) Findable–Accessible–Interoperable–Reusable (FAIR) models, data, and OASIS products. Stakeholders, including researchers, are actively recruited to participate in Theme Teams to help promote a predicted, safe, clean, healthy, resilient, and productive ocean.