Abstract
This study investigates the fabrication of plasmonic optical fiber sensors for glyphosate detection, employing silver thin film coatings deposited via the Tollens' reaction and further enhanced with protective gold plating. Silver films were produced through electroless deposition, forming rough plasmonic surfaces with localized hotspots that amplify the electromagnetic field. Surface roughness effects on the creation of hotspots were first evaluated numerically using the finite element method (FEM) and later experimentally assessed the impact on optical response. Furthermore, to address the inherent susceptibility of silver to oxidation and corrosion, a gold plating was applied using the Kirkendall effect, selectively replacing surface silver atoms with gold. This approach significantly improved the chemical stability of the sensors while preserving their plasmonic properties. This configuration was applied in developing a biosensor, using aptamers, for detecting glyphosate in concentrations ranging from 10(-1) to 10(4) mu g/L. The results demonstrated a sensitivity of 25.08 +/- 0.22 nm/(mu g/L) and a limit of detection (LOD) of 0.04 mu g/L, nearly ten times lower than the European Union's safety limit for glyphosate. Experimental results highlight the potential of this fabrication approach for developing sensitive, stable, and scalable plasmonic sensors tailored for environmental and agricultural monitoring applications.

Abstract
Non-formal education seeks to address the limitations of formal education that do not reach all communities and do not provide all new competencies and capabilities that are essential for the integrated development of communities. The role of non-formal education becomes even more relevant in the context of developing countries where significant asymmetries in access to education emerge. This study adopts the Solutions Story Tracker provided by the Solutions Journalism Network to identify and explore solutions based on journalism stories in the non-formal education field. A total of 256 stories are identified and categorized into 14 dimensions. The findings reveal that practical, participatory, and volunteering dimensions are the three most common dimensions in these non-formal education initiatives. Furthermore, two emerging dimensions related to empowerment and sustainability are identified, allowing us to extend the theoretical knowledge in the non-formal education field. These conclusions are relevant for establishing public policies that can involve greater participation by local communities in non-formal education and for addressing sustainability challenges through bottom-up initiatives.

Abstract
Yield forecasting is of immeasurable value in modern viticulture to optimize harvest scheduling and quality management. The number of inflorescences and flowers per vine is one of the main components and their assessment serves as an early predictor, which can explain up to 85-90% of yield variability. This study introduces a sophisticated framework that integrates the benchmark of different advanced deep learning and classic image processing to automate the segmentation of grapevine inflorescences and the detection of single flowers, to achieve precise, early, and non-invasive yield predictions in viticulture. The YOLOv8n model achieved superior performance in localizing inflorescences ( F1-Score (Box) = 95.9%) and detecting individual flowers (F1-Score = 91.4%), while the YOLOv5n model excelled in the segmentation task ( F1-Score (Mask) = 98.6%). The models demonstrated a strong correlation (R-2 > 90.0%) between detected and visible flowers in inflorescences. A statistical analysis confirmed the robustness of the framework, with the YOLOv8 model once again standing out, showing no significant differences in error rates across diverse grapevine morphologies and varieties, ensuring wide applicability. The results demonstrate that these models can significantly improve the accuracy of early yield predictions, offering a noninvasive, scalable solution for Precision Viticulture. The findings underscore the potential for Computer Vision technology to enhance vineyard management practices, leading to better resource allocation and improved crop quality.

Abstract
Lung cancer stands as the most prevalent and deadliest type of cancer, with adenocarcinoma being the most common subtype. Computed Tomography (CT) is widely used for detecting tumours and their phenotype characteristics, for an early and accurate diagnosis that impacts patient outcomes. Machine learning algorithms have already shown the potential to recognize patterns in CT scans to classify the cancer subtype. In this work, two distinct pipelines were employed to perform binary classification between adenocarcinoma and non-adenocarcinoma. Firstly, radiomic features were classified by Random Forest and eXtreme Gradient Boosting classifiers. Next, a deep learning approach, based on a Residual Neural Network and a Transformer-based architecture, was utilised. Both 2D and 3D CT data were initially explored, with the Lung-PET-CT-Dx dataset being employed for training and the NSCLC-Radiomics and NSCLC-Radiogenomics datasets used for external evaluation. Overall, the 3D models outperformed the 2D ones, with the best result being achieved by the Hybrid Vision Transformer, with an AUC of 0.869 and a balanced accuracy of 0.816 on the internal test set. However, a lack of generalization capability was observed across all models, with the performances decreasing on the external test sets, a limitation that should be studied and addressed in future work.

Abstract
Measuring the density of tartrazine (TZ) powder allowed to develop a protocol for fast preparation of aqueous solutions with a desired concentration. The stability time of these solutions decreases exponentially with the increase of TZ concentration: solutions with TZ concentrations below 25% remain stable for more than 24 h, while the solution with 60% TZ remains stable only for 35 min. To validate the developed protocol, muscle samples were immersed in the 40% TZ solution and, as expected, the tissue transparency increased smoothly and exponentially during the whole treatment of 30 min. The diffusion time of TZ in ex vivo skeletal muscle was quantitatively determined with high accuracy as tau TZ = 5.39 +/- 0.49 min for sample thickness of 0.5 mm. By measuring the refractive index of TZ solutions during preparation, it will be easier to prepare such solutions in a fast manner for future research on tissue optical clearing.

Abstract
Throughout the Virtual Reality (VR) literature, we find different terms to define the same concepts as well as the same terms addressing different concepts. This issue can easily cause misinterpretations and difficulty in the analysis of papers from different authors. This work addresses this terminology confusion through a detailed analysis of current key concepts, how they have been employed, comparing them to other concepts, and proposing adaptations to their definitions to reduce conceptual overlap while preserving the original terms. In this work, we reviewed widely used terms in VR: Fidelity, Realism, Immersion, Presence, and Coherence. We also identified and discussed derivative terms, such as Place Illusion, Plausibility Illusion, Sensorimotor Contingencies, Multisensory, Virtual Content, Objective and Subjective Realism, and Objective and Subjective Internal Coherence. We proposed how these distinct concepts can be separated, merged, and linked, providing a clearer terminology for future use and discussing the implications of this terminology.