Rotations play a detrimental role in achieving ultra-high-performance inertial measurements with an atom interferometer, leading potentially to a total loss of interference contrast and the emergence of dominant phaseshift biases. This becomes particularly significant when considering operation in dynamic conditions such as those encountered by Earth-orbiting satellites in the perspective of future space gravity missions onboarding a cold-atom accelerometer. In this context, we perform an experimental study of the impact of rotation on an atom interferometer and investigate mitigation strategies. Using an original setup in which the well-controlled proof mass of a space electrostatic accelerometer is used as the retroreflection mirror of a cold-atom gravimeter, we are able to measure both the contrast and phase shift of the interferometer especially for low and high angular accelerations. Thanks to an analytical model, we are able to explain the origin of the measured contrast loss and phase shifts arising from the different inertial accelerations. By properly counterrotating the electrostatic proof mass, we demonstrated a contrast, otherwise equal to zero, to a level better than 90%, for both constant angular velocities and for important angular accelerations. For constant angular velocities, our model retrieves the experimental data with an error of 25 mrad, in agreement with the experimental uncertainty. Our results demonstrate the possibility to perform high-performance inertial measurements with a cold-atom interferometer in challenging environments by demonstrating a high contrast recovery and the precise determination of the rotation-induced phase-shift bias.
Crystallographic texture in powder bed fusion by laser beam melting (PBF-LB/M) strongly influences the anisotropic mechanical response of metallic components. However, for Inconel 625 (IN625), it remains insufficiently understood how laser scan-path design governs not only the selection of the dominant texture component, but also the extent to which this component sharpens during solidification. In particular, the respective roles of scan angle, melt-pool geometry, remelting continuity, and scan-vector length (SVL) remain only partially separated in the literature. To address this issue, the present work investigates the influence of laser scan-path design on melt-pool morphology, crystallographic texture, and the resulting mechanical response in PBF-LB/M IN625 through a combined analysis of melt-pool geometry, electron backscatter diffraction, and tensile behaviour. The results show that scan angle primarily controls texture selection along the scanning direction, with dominant (111) and < 001 > components obtained for alpha = 35 degrees and alpha = 90 degrees, respectively, whereas melt-pool geometry and remelting continuity govern texture sharpening. Conditions combining higher overlap (up to 69%), flatter melt-pool bottom profiles, and more continuous remelting produced the strongest textures, with texture indices up to J = 10, while more curved melt-pool geometries and larger effective non-remelted regions promoted competing grain growth and weaker sharpening. Short SVL introduced a distinct band-filling thermal regime that further modified competitive epitaxial growth independently of nominal energy input, increasing the texture index from J = 3.4 at SVL = 0.5 mm to J = 6.0 at SVL = 1.0 mm. These scan-induced texture states translated into measurable mechanical differences along the scanning direction: the < 111 > dominated condition reached a UTS of similar to 910 MPa with 24.5% elongation, compared with similar to 769 MPa and 16.0% for the < 001 > dominated condition. Overall, this work establishes a transferable process-melt-pool-texture-property framework showing that laser scan-path design can be used as a practical lever for crystallographic texture control in PBF-LB/M alloys.
Primary copper production capacity is crucial given future demand and social, environmental, technical, economic, and political constraints, often overlooked in decarbonization pathway models. To address this, we propose a methodology to examine the consistency of the basic drivers of Shared Socioeconomic Pathways (SSPs) for primary copper requirements using the DyMEMDS stock-flow model. Our approach involves projecting primary copper production capacities to 2050 on a mine-by-mine basis, integrating mining industry dynamics based on commercial data. Results indicate significant concerns regarding the consistency of SSPs' basic drivers for copper requirements, revealing potential gaps exceeding 40 Mt in worst-case scenarios. Such discrepancies could impact technology deployments necessary for socioeconomic and decarbonisation assumptions. We recommend that the decarbonization modeling community align scenarios with mining industry constraints. Considering resource efficiency and circular economy strategies is essential for proposing more consistent scenarios to decision-makers, thereby mitigating risks of copper supply shortages hindering climate action.
The increasing availability of Earth observation data offers unprecedented opportunities for large-scale environmental monitoring and analysis. However, these datasets are inherently heterogeneous, stemming from diverse sensors, geographical regions, acquisition times, and atmospheric conditions. Distribution shifts between training and deployment domains severely limit the generalization of pretrained remote sensing models, making unsupervised domain adaptation (UDA) crucial for real-world applications. We introduce FlowEO, a novel framework that leverages generative models for image-space UDA in Earth observation. We leverage flow matching to learn a semantically preserving mapping that transports from the source to the target image distribution. This allows us to tackle challenging domain adaptation configurations for classification and semantic segmentation of Earth observation images. We conduct extensive experiments across four datasets covering adaptation scenarios such as SAR to optical translation and temporal and semantic shifts caused by natural disasters. Experimental results demonstrate that FlowEO outperforms existing image translation approaches for domain adaptation while achieving on-par or better perceptual image quality, highlighting the potential of flow-matching-based UDA for remote sensing.
This study explores how personal factors—normative beliefs, personal norms, ascription of responsibility, and altruistic values—influence Sustainable Entrepreneurial Intentions (SEI). It examines the mediating roles of Attitudes Toward Sustainability (ATS) and Education for Sustainable Entrepreneurship (ESE), and the moderating role of job dissatisfaction. Using a model grounded in the VBN theory and informed by insights from TPB, SCT, and EEM, data from 163 Lebanese participants were analyzed using PLS-SEM. Results show that normative beliefs, ascription of responsibility, personal norms, and altruistic values significantly influence SEI. Education for sustainable entrepreneurship and attitudes toward sustainability serve as partial or full mediators, while job dissatisfaction moderates the effects of normative beliefs and personal norms. Notably, altruistic values had the strongest positive association with SEI, and attitudes toward sustainability fully mediated their effect. The findings highlight the importance of sustainability education and workplace conditions in shaping entrepreneurial motivations.
