Abstract
Radon (Rn-222) is a unique atmospheric tracer, since it is an inert gaseous radionuclide with a predominantly terrestrial source and a short half-life (3.8232 (8) d), enabling quantification of the relative degree of recent (< 21 d) terrestrial influences on marine air masses. High quality measurements of atmospheric radon activity concentration in remote oceanic locations enable the most accurate identification of baseline conditions. Observations of GHGs under baseline conditions, representative of hemispheric background values, are essential to characterise long-term changes in hemispheric-mean GHG concentrations, differentiate between natural and anthropogenic GHG sources, and improve understanding of the global carbon budget.The EU-funded project NuClim (Nuclear observations to improve Climate research and GHG emission estimates) will establish world-leading high-quality atmospheric measurements of radon activity concentration and of selected GHG concentrations (CO2, and CH4) at a remote oceanic location, the Eastern North Atlantic (ENA) facility, managed by the Atmospheric Radiation Measurement (ARM) programme (Office of Science from the U.S. Department of Energy), located on Graciosa Island (Azores archipelago), near the middle of the north Atlantic Ocean. These observations will provide an accurate, time-varying atmospheric baseline reference for European greenhouse gas (GHG) levels, enabling a clearer distinction between anthropogenic emissions and slowly changing background levels. NuClim will also enhance measurement of atmospheric radon activity concentration at the Mace Head Station, allowing the identification of latitudinal gradients in baseline atmospheric composition, and supporting the evaluation of the performance of GHG mitigation measures for countries in the northern hemisphere.The high-quality nuclear and GHG observations from NuClim, and the resulting classification of terrestrial influences on marine air masses, will assist diverse climate and environmental studies, including the study of pollution events, characterisation of marine boundary layer clouds and aerosols, and exploration of the impact of natural planktonic communities on GHG emissions. This poster presents an overview of NuClim, outlines the project objectives and methodologies, and summarises the relevant data products that will be made available to the climate community.Project NuClim received funding from the EURATOM research and training program 2023-2025 under Grant Agreement No 101166515.

Abstract
Project NuClim (Nuclear observations to improve Climate research and GHG emission estimates) aims to use high-quality measurements of atmospheric radon activity concentration and ambient radioactivity to advance climate science and improve radiation protection and nuclear surveillance capabilities. It is supported by new metrological capabilities developed in the EMPIR project 19ENV01 traceRadon. This work reviews the scientific objectives of project NuClim in terms of both climate science and radiological protection, and provides an overview of the NuClim field campaign and the various nuclear measurements being implemented within the scope of the project.

Abstract
User studies are paramount for advancing research in software engineering, particularly when evaluating tools and techniques involving programmers. However, researchers face several barriers when performing them despite the existence of supporting tools. We base our study on a set of tools and researcher-reported barriers identified in prior work on user studies in software engineering. In this work, we study how existing tools and their features cope with previously identified barriers. Moreover, we propose new features for the barriers that lack support. We validated our proposal with 102 researchers, achieving statistically significant positive support for all but one feature. We study the current gap between tools and barriers, using features as the bridge. We show there is a significant lack of support for several barriers, as some have no single tool to support them.

Abstract
Code comprehension is often supported by source code analysis tools that provide more abstract views over software systems, such as those detecting design patterns. These tools encompass analysis of source code and ensuing extraction of relevant information. However, the analysis of the source code is often specific to the target programming language. We propose DP-LARA, a multilanguage pattern detection tool that uses the multilanguage capability of the LARA framework to support finding pattern instances in a code base. LARA provides a virtual AST, which is common to multiple OOP programming languages, and DP-LARA then performs code analysis of detecting pattern instances on this abstract representation. We evaluate the detection performance and consistency of DP-LARA with a few software projects. Results show that a multilanguage approach does not compromise detection performance, and DP-LARA is consistent across the languages we tested it for (i.e., Java and C/C++). Moreover, by providing a virtual AST as the abstract representation, we believe to have decreased the effort of extending the tool to new programming languages and maintaining existing ones.

Abstract
The rapid proliferation of Edge AI applications demands efficient, low-power computing architectures tailored to specific workloads. The RISC-V ecosystem is a promising solution, and has led to a fast growth of implementations based on custom instructions extensions, but with varying degrees of functionality and support which may hinder easy adoption. In this paper, we extensively review existing RISC-V extensions targeting primarily the AI domain and respective compilation flows, highlighting challenges in deployment, usability, and compatibility. We further implement and provide usable containerized environments for two of these works. To address the identified challenges, we then propose an approach for lightweight early validation of custom instructions via source-to-source transformations, without need of compiler modifications. We target our own Single Instruction Multiple Data (SIMD) accelerator, which we integrate into a CORE-V cv32e40px baseline core through custom instructions, and versus which we achieve up to 11.9x speedup for matrix-vector operations.

Abstract
Mobile devices have become integral to our everyday lives, yet their utility hinges on their battery life. In Android apps, resource leaks caused by inefficient resource management are a significant contributor to battery drain and poor user experience. Our work introduces Alpakka, a source-to-source compiler for Android's Smali syntax. To showcase Alpakka's capabilities, we developed an Alpakka library capable of detecting and automatically correcting resource leaks in Android APK files. We demonstrate Alpakka's effectiveness through empirical testing on 124 APK files from 31 real-world Android apps in the DroidLeaks [12] dataset. In our analysis, Alpakka identified 93 unique resource leaks, of which we estimate 15% are false positives. From these, we successfully applied automatic corrections to 45 of the detected resource leaks.