Abstract
Many physical and chemical processes of interest evolve on timescales that push the limits of conventional spectroscopic instrumentation. Indeed, the temporal resolution of standard spectrometers is often insufficient to track these dynamics, which is connected to the fact that most systems rely on frame-based sensors, imposing fundamental constraints on acquisition speed, sensitivity, and data efficiency, frequently limiting practical operation to the kilohertz regime. In this work, we present an approach to circumvent this limitation by developing an event-based spectrometer to enable spectral reconstruction with microsecond temporal resolution by leveraging a Czerny–Turner configuration combined with asynchronous and event-driven sensing. A dedicated signal processing pipeline converts the resulting stream of binary events into calibrated spectra through temporal accumulation, geometric correction, and vertical spatial integration of the spectral line, covering a 234nm bandwidth in the visible range with a spectral resolution of approximately 0.18nm per pixel. Performance characterization under temporally modulated illumination demonstrates that the event-based spectrometer can reconstruct spectra at probing rates of up to tens of kilohertz, far exceeding the practical limits of a conventional frame-based spectrometer operated in parallel, while accurately preserving spectral peak positions and relative spectral features.Finally, to further illustrate its potential applications, the system is validated in a microfluidic experiment integrated into an inverted microscope, where spectral changes induced by an absorbing dye are tracked with higher temporal fidelity and resolution comparing with the frame-based approach. These results establish event-based spectroscopy as a promising paradigm for real-time, high-temporal-resolution spectral measurements in dynamic and low-light applications.

Abstract

Abstract
A superfluid flows without friction below a critical velocity, exhibiting zero drag force on impurities. Above this threshold, superfluidity breaks down, and the internal energy is redistributed into incoherent excitations such as vortices. We demonstrate that a mobile, finite-mass impurity immersed in a flowing two-dimensional paraxial superfluid of light can swim against the superfluid current when the critical velocity is exceeded. This self-propulsion is achieved by the periodic emission of vortex-antivortex pairs downstream, which impart an upstream recoil momentum that results in a net propulsive force. Analogous to biological systems that minimize effort by exploiting wake turbulence, the impurity harnesses this vortex backreaction as a passive mechanism of locomotion. Based on a simple theoretical model, we quantitatively describe how this mechanism depends on the impurity geometry and the surrounding flow velocity. Our findings establish a fundamental link between internal-energy dissipation in quantum fluids and concepts of self-propulsion in active-matter systems and open new possibilities for exploiting quantum vortices for controlled transport at the microscale.

Abstract
Circular Economy is based on implementation of R-strategies to narrow or close the loop of material flows and to minimize raw material consumption by extending the life cycle of materials. Since this approach is expanding from individual organizational actions to a collaborative approach, the objective of this paper is to analyse the role of digital technologies such as AI and cloud platforms in facilitating and changing the collaboration between stakeholders to improve sustainability. This study adopts a qualitative multi case study methodology, using surveys, interviews and document analysis from 10 new ventures in the agri-food ecosystem supported by the cascade funding programme. The results show that collaboration among actors is changed by the different technologies and strategic drivers of circular economy in the considered ecosystem.

Abstract

Abstract
Ambient gamma dose rate represents the integrated near-surface gamma radiation field resulting from contributions of terrestrial radionuclides and radon progeny, secondary cosmic radiation, and atmospheric radiation sources. Continuous monitoring of ambient gamma dose rate constitutes a fundamental component of radiological early-warning systems, as it provides a direct operational proxy for external radiation exposure to population. Time series of ambient gamma dose rate exhibit variability over a wide range of temporal scales, including short-term anomalies driven by meteorological processes, geophysical conditions, or anthropogenic influences. Accurate characterisation of these anomalies, and robust discrimination between natural drivers - such as soil–atmosphere exchange processes, boundary-layer dynamics, and hydrometeorological forcing - and potential anthropogenic contributions, is essential for enhancing early-warning capabilities and improving the detection of anomalous radioactive releases. A key challenge in this context is the scarcity of high-resolution, high-quality collocated meteorological observations required to support such analyses.This study presents a detailed characterization of anomalies in ambient gamma dose rate using comprehensive meteorological information and high-resolution (1-min) gamma dose-rate measurements from the Eastern North Atlantic (ENA) observatory, part of the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Program. Through the joint analysis of gamma radiation and a broad set of meteorological parameters - including precipitation, eddy covariance fluxes, aerosol properties, and lidar derived atmospheric structure - we identify and classify distinct types of short-term gamma radiation anomalies. These include precipitation-induced enhancements, quasi-daily anomalies associated with stable nocturnal boundary-layer conditions and near-surface radon accumulation, and anomalies linked to long-range transported dust events. This AI-ready, supervised dataset enables detailed investigation and modelling of ambient gamma dose-rate variability in the Azores and provides a transferable framework for training machine-learning algorithms to automatically classify gamma radiation anomalies at monitoring sites lacking comprehensive meteorological instrumentation. The present study is part of project NuClim (Nuclear observations to improve Climate research and GHG emission estimates). Project NuClim received funding from the EURATOM research and training program 2023-2025 under Grant Agreement No 101166515). The NuClim field campaign at the Eastern North Atlantic, Graciosa Island ARM Observatory is supported by the U.S. Department of Energy (DOE), Office of Science, through the ARM Program.