Abstract
Over the past 18 months, we have performed hundreds of temperature characterizations of fiber Bragg gratings inscribed in different germanium-doped silica glass fibers. Under experimental conditions, the main conclusions are as follows: the temperature dependence of the temperature gauge factor or the normalized temperature sensitivity, K-T, was found to be quadratic in the -50-200 degrees C range, while it may be considered linear for the -20-100 degrees C range; K-T values at 20 degrees C vary from 5.176 x 10(-6) K-1, for a B/Ge co-doped fiber up to 6.724 x 10(-6) K-1, for a highly Ge-doped fiber; K-T does not depend on the hydrogen-loading process or the gratings coupling strength; K-T is essentially independent of wavelength in the 1500-1600 nm range, its value being accurately determined with a relative error similar to 0.2%; based on the accurate value of K-T = 6.165 x 10(-6) K-1, at 20 degrees C, obtained for gratings inscribed in the SMF-28 fiber, we calculated a value of 19.4 x 10(-6) K-1 for the thermo-optic coefficient of bulk germanium glass; and gratings produced by femtosecond-laser radiation and UV-laser radiation exhibit comparable values of K-T. The previous achievements allow, by having knowledge of K-T for a single grating, the accurate determination of the temperature dependence of the Bragg wavelength for any other grating inscribed in the same fiber; the presented methodology enables one to determine the unknown gratings' temperature sensitivity, typically with an error of 0.01 pm/degrees C, being, therefore, very useful in research labs and computer simulations. Thus, expressions for the temperature dependence of K-T for gratings inscribed in several fibers are given, as well as an expression for K-T as a function of the effective refractive index. We have also fully analyzed the potential sources of error in K-T determination.

Abstract
Technological advances in global communications depend significantly on robust and efficient long-distance infrastructures. One notable example is the submarine cable network. Installed on the ocean floor, these cables use fiber optic technology to transmit large volumes of data at high speed and low latency between continents. Beyond their primary communication function, recent innovations allow these cables to serve as Distributed Acoustic Sensing (DAS) systems, effectively converting tens of kilometers of passive fiber into massive, coherent arrays of vibration sensors. The primary objective of this project is to enhance maritime surveillance capabilities by integrating DAS technology with advanced kinematic modeling. This paper establishes a rigorous physical and mathematical framework for interpreting the acoustic signatures of surface vessels detected by bottom-mounted fibers. We derive the complete opto-acoustic transfer function, formulate the hyperbolic moveout equations based on a moving point-source solution to the wave equation, and implement a stochastic inversion scheme using Differential Evolution. By optimizing a correlation-based loss function, we demonstrate the ability to recover vessel trajectory, speed, and depth from complex interferometric patterns with speed estimation errors consistently below 1%. This approach allows for the extraction of quantitative physical parameters from raw optical data, bridging the gap between photonics and hydroacoustics.

Abstract
Blending green hydrogen into gas networks is subject to strict quality regulation. As Europe diversifies its Liquefied Natural Gas supply, existing literature often ignores the extreme chemical variability of these sources by assuming a static composition. This study proposes a nonlinear steady-state optimization model, strictly adhering to European standards, to maximize hydrogen injection across eight real-world LNG profiles. Results reveal severe sensitivity to the carrier gas: lean gas (Trinidad and Tobago, USA) restricts maximum hydrogen integration to 8.89% and 11.40% by volume due to insufficient heavy hydrocarbons. Conversely, carbon-rich gas (Nigeria) allows up to 20.10%, although inert gases degrade this capacity. Ultimately, this 20.10% maximum blend yields only a 6.77% emission reduction. The imported chemical profile dictates absolute limits, proving that static universal hydrogen quotas are thermodynamically unachievable year-round without continuous dynamic quality tracking. © 2026

Abstract
Food Loss and Waste (FLW) remain major challenges for global food security, environmental sustainability, and economic stability, with nearly one-third of food produced each year being lost or wasted. Although many technologies exist to mitigate FLW, they are often assessed separately, making it difficult for decision-makers to compare options and select solutions suited to specific contexts. This research introduces an explainable decision support system (XDSS) that helps prioritise FLW mitigation strategies while accounting for uncertainty in stakeholder preferences. The proposed framework combines the Best-Worst Method (BWM) with Stochastic Multi-criteria Acceptability Analysis for Group Decision-Making (SMAA-2) to produce transparent and uncertainty-aware rankings. It evaluates one hundred FLW mitigation strategies across five contextual criteria: geographic fit, product category, food supply-chain stage, stakeholder role, and technology type. Rather than producing a single fixed ranking, the system generates probabilistic rank-acceptability profiles that indicate the likelihood of each strategy performing well under different preference conditions. Illustrative scenarios demonstrate that the framework can translate qualitative user preferences into robust prioritisation outcomes, with leading alternatives achieving first-rank-acceptability levels between 62% and 74%. These results indicate that the system can support clearer and more flexible decision-making when preferences are incomplete, inconsistent, or uncertain. Although the current results are based on simulated structured cases, the proposed XDSS provides a transparent methodological foundation for future real-world validation and operational deployment. The framework offers practical value for selecting FLW technologies and for policy planning, contributing to more sustainable food systems and supporting progress toward SDG 12.3.

Abstract

Abstract
Small-and-medium-sized enterprises (SMEs) increasingly depend on business partnerships to access markets and scale operations, yet they often face trust barriers during contract formation due to the complexity of the verification of their cybersecurity posture and compliance status by their partners. This problem is intensified by rising regulatory expectations, notably the EU Cyber Resilience Act (CRA), which many SMEs struggle to interpret and operationalize under constraints of budget, skills, and fragmented responsibilities. This study adopts a Design Science Research approach to blueprint and evaluate a lightweight mapping framework that links commonly implemented security controls to CRA requirements and to widely recognized benchmarks (ISO/IEC 27001 and CIS). Grounded in Institutional Theory and Socio-Technical Systems Theory, the artefact translates regulatory obligations into actionable, evidence-backed controls and produces partner-facing outputs that support transparency in negotiations and service level agreements. The framework is iteratively co-created with a multidisciplinary expert community. Expected contributions include a practical mechanism for making cybersecurity maturity visible, accelerating partnership formation, and enabling sustainable interorganizational relationships while remaining feasible for resource-constrained SMEs.