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.

Abstract
DIn the digital transformation of industrial sectors, data is a high-value business asset. How companies manage data between systems within the organization or through networks of business partners impacts their competitive factor. Technological maturity may imply several adversities, such as the lack of interoperability standards for simple and transparent data exchange. This paper presents an architecture that enables secure exchanges of supply chain orders between textile and clothing companies. This architecture is based on Electronic Business (eBIZ) 4.0 and International Data Spaces (IDS) frameworks, fostering trust and widespread adoption of platforms in the industry sector, particularly when handling sensitive supply chain information. The architecture was implemented and validated in 3 use cases with Enterprise Resource Plannings (ERPs) from the same vendor, different vendors, and communication from a ERP to a Web portal. Implementing the proposed architecture impacted efficiency, transparency, and accountability within the supply chain network. The lead times for purchases, provisioning, and the number of additional information requests in the ordering were reduced. In subcontracting, a reduction in non-conformities and an overall improvement in delivery times were verified. Moreover, logistics operations and communication with subcontractors were optimized, leading to faster order reception and reducing informal contacts.

Abstract
This study investigates the antifragility of organizations, especially in strategic sectors highly exposed to disruptive events. Based on a qualitative approach with case studies in the wine and textile sectors in Portugal, the findings indicate that financial and market strength, as resilience capabilities, operate interdependently and are reinforced by digital maturity and supply chain integration. Companies with financial robustness and strong market intelligence tend to be more agile in strategically investing and reallocating resources during crises. The research adopts an expanded definition of antifragility, which incorporates resilience, innovation, and strategic reconfiguration in the face of disruptions. It concludes that organizational antifragility results from the articulation of financial resources, market intelligence, and digital collaboration, offering a sustainable competitive advantage in the face of uncertainty. The study contributes to theoretical debates and provides practical recommendations for managers and policymakers.

Abstract
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Abstract
The present study investigates the impact of a virtual Vernier effect in the fundamental state and first harmonic to enhance the sensitivity of a strain sensor. A fibre loop mirror (FLM) combined with an internal elliptical cladding (IEC) fibre section was used as the sensor, while a virtual reference spectrum derived from theoretical equations enabled the Vernier effect. For the individual sensor, a sensitivity of 15.39 ± 0.03 pmµe?¹ and a free spectral range (FSR) of 4.22 ± 0.01 nm are obtained. For the virtual Vernier effect, a detuning of 0.15 m is used in both states, resulting in an FSR of 30.2 ± 0.1 nm. A sensitivity of 109.8 ± 0.7 pmµe?¹ is achieved for the fundamental state, associated with a figure of merit (FoM) of 1.01 ± 0.03, and a sensitivity of 230 ± 2 pmµe?¹ for the first harmonic, associated with a figure of merit (FoM) of 2.1 ± 0.1. This work demonstrates the feasibility of implementing the virtual Vernier effect, not only enabling Vernier effect amplification but also reducing implementation complexity and increasing system robustness under harsh conditions.