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The authors present a scientometric procedure comparing engineering education (EE) publication output in different countries. Selected European countries were compared using a snapshot of authors published in EE journals during a two-year period – 895 in all. The entire publication careers of these authors – 39,322 publications – were analysed to determine the breakdown of educational and non-educational publications. Spain and the UK produced the most publications, and we propose explanations based on relevant national policies. France, Germany, and Italy had notably fewer EE publications relative to their general engineering and science research outputs, whereas Portugal, Ireland, and three Nordic countries were the opposite. Countries varied widely in the ratio of educational to non-educational publications. Non-educational publications typically had a greater impact on authors' h-index values. We believe this procedure can longitudinally map and compare EE publication output in and across Europe, providing a valuable resource for policy-makers and researchers in the region. [ABSTRACT FROM AUTHOR]

Photoexcited molecular trajectories on potential energy surfaces (PESs) prior to thermalization are intimately connected to the photochemical reaction outcome. The excited‐state trajectories of a diplatinum complex featuring photo‐activated metal–metal σ‐bond formation and associated Pt−Pt stretching motions were detected in real time using femtosecond wide‐angle X‐ray solution scattering. The observed motions correspond well with coherent vibrational wavepacket motions detected by femtosecond optical transient absorption. Two key coordinates for intersystem crossing have been identified, the Pt−Pt bond length and the orientation of the ligands coordinated with the platinum centers, along which the excited‐state trajectories can be projected onto the calculated PESs of the excited states. This investigation has gleaned novel insight into electronic transitions occurring on the time scales of vibrational motions measured in real time, revealing ultrafast nonadiabatic or non‐equilibrium processes along excited‐state trajectories involving multiple excited‐state PESs. [ABSTRACT FROM AUTHOR]

Photoexcited molecular trajectories on potential energy surfaces (PESs) prior to thermalization are intimately connected to the photochemical reaction outcome. The excited‐state trajectories of a diplatinum complex featuring photo‐activated metal–metal σ‐bond formation and associated Pt−Pt stretching motions were detected in real time using femtosecond wide‐angle X‐ray solution scattering. The observed motions correspond well with coherent vibrational wavepacket motions detected by femtosecond optical transient absorption. Two key coordinates for intersystem crossing have been identified, the Pt−Pt bond length and the orientation of the ligands coordinated with the platinum centers, along which the excited‐state trajectories can be projected onto the calculated PESs of the excited states. This investigation has gleaned novel insight into electronic transitions occurring on the time scales of vibrational motions measured in real time, revealing ultrafast nonadiabatic or non‐equilibrium processes along excited‐state trajectories involving multiple excited‐state PESs. [ABSTRACT FROM AUTHOR]

Two-coordinate transition metal complexes are exciting candidates for single-molecule magnets (SMMs) because their highly axial coordination environments lead to sizeable magnetic anisotropy. We report a series of five structurally related two-coordinate Fe(II) m-terphenyl complexes (4-R-2,6-Xyl2C6H2)2Fe [R = tBu (1), SiMe3 (2), H (3), Cl (4), CF3 (5)] where, by changing the functionalisation of the para-substituent (R), we alter their magnetic function. All five complexes are field-induced single-molecule magnets, with relaxation rates that are well-described by a combination of direct and Raman mechanisms. By using more electron donating R groups we were able to slow the rate of magnetic relaxation. Our ab initio calculations predict a large crystal field splitting (>850 cm−1) and sizeable zero-field splitting parameters (ca. −60 cm−1, ‖E‖ < 0.2 cm−1) for 1–5. These favourable magnetic properties suggest that m-terphenyl ligands have untapped potential as chemically versatile ligands able to impose highly axial crystal fields. [ABSTRACT FROM AUTHOR]

Monte Carlo methods are widespread in geophysics and have proved to be powerful in non-linear inverse problems. However, they are associated with significant practical challenges, including long calculation times, large output ensembles of Earth models, and difficulties in the appraisal of the results. This paper addresses some of these challenges using generative models, a family of tools that have recently attracted much attention in the machine learning literature. Generative models can, in principle, learn a probability distribution from a set of given samples and also provide a means for rapid generation of new samples which follow that approximated distribution. These two features make them well suited for application to the outputs of Monte Carlo algorithms. In particular, training a generative model on the posterior distribution of a Bayesian inference problem provides two main possibilities. First, the number of parameters in the generative model is much smaller than the number of values stored in the ensemble, leading to large compression rates. Secondly, once trained, the generative model can be used to draw any number of samples, thereby eliminating the dependence on an often large and unwieldy ensemble. These advantages pave new pathways for the use of Monte Carlo ensembles, including improved storage and communication of the results, enhanced calculation of numerical integrals, and the potential for convergence assessment of the Monte Carlo procedure. Here, these concepts are initially demonstrated using a simple synthetic example that scales into higher dimensions. They are then applied to a large ensemble of shear wave velocity models of the core–mantle boundary, recently produced in a Monte Carlo study. These examples demonstrate the effectiveness of using generative models to approximate posterior ensembles, and indicate directions to address various challenges in Monte Carlo inversion. [ABSTRACT FROM AUTHOR]

Benefits of industry engagement in engineering education for various stakeholders are well known, but there can be significant obstacles to initiating engagement. Using social capital theory, this study investigated how stakeholders initiate industry engagement in engineering education, and the main barriers from the stakeholders' perspectives. Semi-structured interviews were held with 21 professionals involved in the engineering industry (including academics and industry personnel). Inductive analysis showed industry engagement was initiated by all stakeholders, although reasons differed. Relationships between individual academics and their industry contacts were often key. Relying on these relationships was problematic when individuals changed roles or organisations. Relationships were also maintained through ongoing agreements or facilitated through third-party organisations. Key barriers included lack of invitations to industry stakeholders from universities, and unclear benefits to industry stakeholders of long-term engagement. The success of third parties in facilitating engagement may provide a framework for facilitating successful ongoing engagement. [ABSTRACT FROM AUTHOR]

Creativity is an important skill for engineers but many students face a lack of experience in idea generation, often compounded by low-self efficacy towards creativity. Providing students with online training modules has been suggested as one solution. However, the literature demonstrates using a computer can influence performance and self-efficacy in a different manner to using pen-and-paper. There is a current lack of research on whether computers may influence self-efficacy during idea generation tasks. Two experiments involving computer-based and pen-and-paper groups were designed. Groups were provided with templates that guided them through the process of applying an idea generation technique and were given sixteen minutes to generate ideas to solve a presented problem. Results revealed that students' performance was similar and using a computer does not negatively influence self-efficacy. This shows that students can engage in idea generation learning tasks using computer-based modes without negatively influencing performance or self-efficacy, suggesting an avenue for educators to engage students with building creativity skills. [ABSTRACT FROM AUTHOR]

Aluminosilicate glasses and melts are of paramount importance for geo- and materials sciences. They include most magmas, and are used to produce a wide variety of everyday materials, from windows to smartphone displays. Despite this importance, no general model exists with which to predict the atomic structure, thermodynamic and viscous properties of aluminosilicate melts. To address this, we introduce a deep learning framework, 'i-Melt', which combines a deep artificial neural network with thermodynamic equations. It is trained to predict 18 different latent and observed properties of melts and glasses in the K 2 O-Na 2 O-Al 2 O 3 -SiO 2 system, including configurational entropy, viscosity, optical refractive index, density, and Raman signals. Viscosity can be predicted in the 100–1015 log 10 Pa·s range using five different theoretical frameworks (Adam-Gibbs, Free Volume, MYEGA, VFT, Avramov-Milchev), with a precision equal to, or better than, 0.4 log 10 Pa·s on unseen data. Density and optical refractive index (through the Sellmeier equation) can be predicted with errors equal or lower than 0.02 and 0.006, respectively. Raman spectra for K 2 O-Na 2 O-Al 2 O 3 -SiO 2 glasses are also predicted, with a relatively high mean error of ∼25% due to the limited data set available for training. Latent variables can also be predicted with good precisions. For example, the glass transition temperature, T g , can be predicted to within 19 K, while the melt configurational entropy at the glass transition, Sconf(T g) , can be predicted to within 0.8 J mol−1 K−1. Applied to rhyolite compositions, i-Melt shows that the rheological threshold separating explosive and effusive eruptions correlates with an increase in the fraction of non-bridging oxygens in rhyolite melts as their alkali/Al ratio becomes larger than 1. Exploring further the effect of the K/(K + Na) ratio on the properties of alkali aluminosilicate melts with compositions varying along a simplified alkali magmatic series trend, we observe that K-rich melts have systematically different structures and higher viscosities compared to Na-rich melts. Combined with the effects of the K/(K + Na) ratio on other parameters, such as the solubility, solution mechanisms and speciation of volatile elements, this could ultimately influence the eruptive dynamics of volcanic systems emitting Na-rich or K-rich alkali magmas. [ABSTRACT FROM AUTHOR]

Glacial isostatic adjustment (GIA) modeling is not only useful for understanding past relative sea‐level change but also for projecting future sea‐level change due to ongoing land deformation. However, GIA model predictions are subject to a range of uncertainties, most notably due to uncertainty in the input ice history. An effective way to reduce this uncertainty is to perform data‐model comparisons over a large ensemble of possible ice histories, but this is often impossible due to computational limitations. Here we address this problem by building a deep‐learning‐based GIA emulator that can mimic the behavior of a physics‐based GIA model while being computationally cheap to evaluate. Assuming a single 1‐D Earth rheology, our emulator shows 0.54 m mean absolute error on 150 out‐of‐sample testing data with <0.5 s emulation time. Using this emulator, two illustrative applications related to the calculation of barystatic sea level are provided for use by the sea‐level community. Plain Language Summary: Piecing together the history of ice sheet change during past glacial cycles is not only important for understanding past sea‐level change but also for predicting how ongoing glacial rebound contributes to future sea‐level change. Traditionally, a physics‐based "sea‐level model" is used to predict the sea‐level change associated with a particular reconstruction of past ice sheet change and compare the results with geological records of past sea level. However, a fundamental limitation of this approach is the need to compute sea‐level change for a large number of plausible ice histories, which is often prohibited by the computational resources required to repeatedly solve the complex physical equations. In this paper, we describe a machine‐learning‐based statistical model that can mimic the behavior of a physics‐based sea‐level model. This statistical model is computationally cheap and we demonstrate that it is able to accurately predict global sea‐level change for a suite of 150 "unseen" ice histories. Our statistical model predicts sea‐level change 100–1,000 times faster than a physics‐based model, making it an ideal tool for investigating and improving our understanding of global ice sheet change. Key Points: The first attempt to build a deep‐learning based Glacial isostatic adjustment (GIA) emulator that can accurately predict global sea‐level change based on a given ice modelThis emulator (GEORGIA) can predict global sea‐level change history within 0.5 s with minor emulation errorThis GIA emulator along with two illustrative applications are available for use by the wider sea‐level community [ABSTRACT FROM AUTHOR]

Access to interactive, simulated work environments through virtual reality (VR) has potential in teaching safety in design (SiD) to engineering students with reduced risk, cost and inconvenience. However, there is limited understanding of what students learn from immersive VR interventions, or the outcomes from specific learning activities. This study investigated engineering students' SiD learning in engineering design units at an Australian university. Informed by blended cognitivist-constructivist and situative perspectives, the units embedded static visualisation, dynamic visualisation through VR simulation, and group discussion, within an authentic hazard identification and analysis process. Observational data and student performances were analysed. Students' hazard comprehension differed by type of hazard and by learning activity. Students demonstrated understanding of SiD processes, terminology, and relevance of these to engineering design. We conclude that VR simulation is an effective learning tool, however the combination of learning elements was important to achieve learning outcomes. [ABSTRACT FROM AUTHOR]

Concerns raised by Okazaki & Ueda (2022) on the paper by Sambridge et al. (2022) are addressed. Two issues are discussed and some new numerical results presented. The first concerns whether the properties of the Wasserstein time-series misfit introduced in our earlier paper will translate to model space non-uniqueness in a seismic waveform inversion setting. It is argued that this is unlikely, given the special conditions, which must exist between all observed/predicted seismic waveform pairs for non-uniqueness to result. The second issue discussed is the efficacy of using the Sliced Wasserstein algorithm of Bonneel et al. (2015) as an alternate to the marginal Wasserstein algorithm, as proposed by Okazaki & Ueda (2022). It is argued that for optimization-based waveform fitting, the Sliced Wasserstein algorithm is a viable alternate provided care is taken to ensure that conditions arise which do invalidate analytical derivative expressions of the resulting Wasserstein misfit. In practice, this would likely mean recasting the 2D Optimal Transport problem posed in our earlier paper onto unstructured grids. [ABSTRACT FROM AUTHOR]

We propose a new approach to measuring the agreement between two oscillatory time-series, such as seismic waveforms, and demonstrate that it can be used effectively in inverse problems. Our approach is based on Optimal Transport theory and the Wasserstein distance, with a novel transformation of the time-series to ensure that necessary normalization and positivity conditions are met. Our measure is differentiable, and can readily be used within an optimization framework. We demonstrate performance with a variety of synthetic examples, including seismic source inversion, and observe substantially better convergence properties than achieved with conventional L 2 misfits. We also briefly discuss the relationship between Optimal Transport and Bayesian inference. [ABSTRACT FROM AUTHOR]

High‐energy X‐ray scattering and pair distribution function analysis (HEXS/PDF) is a powerful method to reveal the structure of materials lacking long‐range order, but is underutilized for molecular complexes in solution. We demonstrate the application of HEXS/PDF with 0.26 Å resolution to uncover the solution structure of five bimetallic CuI/RuII/OsII complexes. HEXS/PDF of each complex in acetonitrile solution confirms the pairwise distances in the local coordination sphere of each metal center as well as the metal⋅⋅⋅metal distances separated by over 12 Å. The metal⋅⋅⋅metal distance detected in solution is compared with that from the crystal structure and molecular models to confirm that distortions to the metal bridging ligand are unique to the solid state. This work presents the first example of observing sub‐Ångström conformational differences by direct comparison of solution phase and solid‐state structures and shows the potential for HEXS/PDF in the determination of solution structure of single molecules. [ABSTRACT FROM AUTHOR]

High‐energy X‐ray scattering and pair distribution function analysis (HEXS/PDF) is a powerful method to reveal the structure of materials lacking long‐range order, but is underutilized for molecular complexes in solution. We demonstrate the application of HEXS/PDF with 0.26 Å resolution to uncover the solution structure of five bimetallic CuI/RuII/OsII complexes. HEXS/PDF of each complex in acetonitrile solution confirms the pairwise distances in the local coordination sphere of each metal center as well as the metal⋅⋅⋅metal distances separated by over 12 Å. The metal⋅⋅⋅metal distance detected in solution is compared with that from the crystal structure and molecular models to confirm that distortions to the metal bridging ligand are unique to the solid state. This work presents the first example of observing sub‐Ångström conformational differences by direct comparison of solution phase and solid‐state structures and shows the potential for HEXS/PDF in the determination of solution structure of single molecules. [ABSTRACT FROM AUTHOR]

Metal-metal interactions, pair distribution function analysis, structure elucidation, X-ray diffraction, X-ray scattering Keywords: metal-metal interactions; pair distribution function analysis; structure elucidation; X-ray diffraction; X-ray scattering EN metal-metal interactions pair distribution function analysis structure elucidation X-ray diffraction X-ray scattering 1 1 1 01/20/22 20220126 NES 220126 B The solution-phase molecular structure b of five redox- and photo-active bimetallic complexes was directly probed using high-energy X-ray scattering and atomic pair distribution function analysis in the Research Article by Xiaosong Li, Karen L. Mulfort et al. (e202111764). Cover Picture: Bimetallic Copper/Ruthenium/Osmium Complexes: Observation of Conformational Differences Between the Solution Phase and Solid State by Atomic Pair Distribution Function Analysis (Angew. [Extracted from the article]

Metal-metal interactions, pair distribution function analysis, structure elucidation, X-ray diffraction, X-ray scattering Titelbild: Bimetallic Copper/Ruthenium/Osmium Complexes: Observation of Conformational Differences Between the Solution Phase and Solid State by Atomic Pair Distribution Function Analysis (Angew. Keywords: metal-metal interactions; pair distribution function analysis; structure elucidation; X-ray diffraction; X-ray scattering EN metal-metal interactions pair distribution function analysis structure elucidation X-ray diffraction X-ray scattering 1 1 1 01/20/22 20220126 NES 220126 B Die Molekülstrukturen b von fünf redox- und photoaktiven Bimetallkomplexen in der Lösungsphase wurden im Forschungsartikel von Xiaosong Li, Karen L. Mulfort und Mitarbeitern mittels Hochenergie-Röntgenstreuung und paarweiser Atomverteilungsfunktion aufgedeckt (e202111764). [Extracted from the article]

• Equation of state (EOS) modelling is a powerful tool to estimate mineral properties. • Conventional explicit EOSs which are assumed to follow certain form provide a biased estimate of uncertainties. • Experimental errors, data inconsistency and sparsity all contribute to uncertainties in mineral seismic properties. • Neural networks based approach captures full uncertainties, highlights data gaps and identifies data inconsistencies. • The approach should improve uncertainty quantification in interpretations of seismic data. Interpretation of information available from seismic data in terms of temperature and composition requires an understanding of the physical properties of minerals, in particular, the elastic properties of candidate Earth minerals at the relevant (here, lower mantle) pressure and temperature. A common practice for the bulk elastic properties is to measure volume at a range of pressures and temperatures using experiments or computational methods. These datasets are then typically fit to a pre-determined functional form, or equation of state to allow computation of elastic properties at any other pressure or temperature. However, errors, both random and systematic, limitations in the number of data and choice of pressure marker and scale, as well as different functional forms of equations of state, all contribute to the uncertainties in mineral seismic properties. In an attempt to present a more comprehensive view of these uncertainties, we use neural-network based techniques to infer the relationship among: pressure, temperature, volume, bulk modulus, and thermal expansivity of MgO. We illustrate our approach on experimental data, but an extension to ab initio data is straightforward. The type of neural network used is called a Mixture Density Network (MDN) which is a combination of a conventional feed-forward neural network and a mixture model that consists of Gaussian functions. MDNs are capable of approximating arbitrary probability density functions, which allows us to compute the uncertainties in the predicted equations of state. Since the networks interpolate locally between input samples, pressure-volume-temperature relations are implicitly learned from data without imposing any explicit thermodynamic assumptions or ad-hoc relationships. We use the partial derivatives of the mapping between inputs (pressure and temperature) and output (volume) to compute the isothermal bulk modulus and thermal expansivity. Flexibility of the MDNs allows us to investigate the uncertainty due to certain data in one region of pressure-temperature space without influencing the posterior probability density everywhere. In general, we find that the elastic properties of MgO are well-constrained by experimental data. However, our study highlights regions in which sparse or inconsistent data lead to poorly constrained elastic properties, namely: at low pressure and high temperature (<25 GPa and >1500 K), and temperatures above 2700 K. While the former conditions are likely not important for the Earth's lower mantle, they are relevant in other planetary bodies such as the Moon and Mars. Comparison with conventional equation of state forms shows that assuming a certain functional form of the pressure-volume-temperature relationship leads to potential bias in uncertainty quantification, because the uncertainties are then specific to the underlying form. In combination with data sets of other lower mantle minerals, this technique should improve uncertainty quantification in interpretations of seismic data. [ABSTRACT FROM AUTHOR]