Abstract
Estuaries are dynamic ecosystems where freshwater and seawater interact, shaping complex hydrodynamic and environmental processes. Traditional single-node monitoring systems, while informative, lack the spatial resolution necessary to fully capture these dynamics. This study presents the development and deployment of a dual-node synchronized wireless sensor network for real-time environmental monitoring in the Cavado Estuary, Portugal. The network architecture integrates low-power embedded systems, a synchronized radiofrequency network, and a web-based data visualization platform. Two monitoring nodes, deployed 675 meters apart, operate in a synchronous cycle to measure hydrostatic pressure and water temperature, demonstrating the feasibility of synchronized environmental sensing. The collected data validated network synchronization, revealing a 30-minute delay in tidal propagation between nodes and highlighting temperature variations influenced by estuarine hydrodynamics. Additionally, long-term observations captured seasonal trends, tidal influences, and extreme weather events such as Storm Kirk. The study also evaluated the system's energy efficiency, confirming the solar panel's capacity to sustain continuous operation and estimating battery life expectancy under different network configurations. This work advances synchronized monitoring networks by providing a scalable, low-cost solution for studying marine environments. The proposed system enables more precise quantification of oceanic influences on estuarine conditions, particularly regarding tidal propagation and phase differences, supporting more effective ecosystem management and understanding.

Abstract
Obstructive sleep apnea is a heterogeneous sleep disorder with varying phenotypes. Several studies have already performed cluster analyses to discover various obstructive sleep apnea phenotypic clusters. However, the selection of the clustering method might affect the outputs. Consequently, it is unclear whether similar obstructive sleep apnea clusters can be reproduced using different clustering methods. In this study, we applied four well-known clustering methods: Agglomerative Hierarchical Clustering; K-means; Fuzzy C-means; and Gaussian Mixture Model to a population of 865 suspected obstructive sleep apnea patients. By creating five clusters with each method, we examined the effect of clustering methods on forming obstructive sleep apnea clusters and the differences in their physiological characteristics. We utilized a visualization technique to indicate the cluster formations, Cohen's kappa statistics to find the similarity and agreement between clustering methods, and performance evaluation to compare the clustering performance. As a result, two out of five clusters were distinctly different with all four methods, while three other clusters exhibited overlapping features across all methods. In terms of agreement, Fuzzy C-means and K-means had the strongest (kappa = 0.87), and Agglomerative hierarchical clustering and Gaussian Mixture Model had the weakest agreement (kappa = 0.51) between each other. The K-means showed the best clustering performance, followed by the Fuzzy C-means in most evaluation criteria. Moreover, Fuzzy C-means showed the greatest potential in handling overlapping clusters compared with other methods. In conclusion, we revealed a direct impact of clustering method selection on the formation and physiological characteristics of obstructive sleep apnea clusters. In addition, we highlighted the capability of soft clustering methods, particularly Fuzzy C-means, in the application of obstructive sleep apnea phenotyping.

Abstract
This paper presents the preliminary findings from the initial research phase of a PhD project that explores the relationship between performative arts and technology. This art-based research critically examines concepts of autonomy, control, and theatricality in puppets empowered by autonomous systems. The project is organized into a three-phase methodological framework, of which the first phase involved conducting interviews with professional puppeteers from diverse cultural backgrounds. This methodological framework was designed to address the first of two research questions, which address the dynamics between the puppeteer and the puppet. After the qualitative analysis of the interviews, the overall findings display not only the current use of technological elements and devices in puppetry practices but also the puppeteers perspectives on enhancing the puppet autonomy for their performances. Moreover, the results reveal the artists' concerns about technological applicability, specifically about the role of human presence and its subsequent replacement, and the preservation of puppetry principles. Conclusively, these preliminary results present a framework where the intertwining between puppetry and robotics offers groundbreaking perspectives for mutual growth.

Abstract
This work demonstrates the sensitivity dependence on the chosen cavity length for the sensor Fabry-Perot interferometer (FPI), where the smaller cavity exhibits a higher magnification factor compared to the situation when the cavity length is larger than the reference interferometer.

Abstract
This work presents a rectangular large core area (R-LCA) PCF with a cladding diameter of 125 mu m, a pitch of 9 mu m, and a hole diameter of 8 mu m. The guided modes were obtained through numerical simulations using COMSOL Multiphysics. This R-LCA-PCF has the potential to be used for refractive index sensing.

Abstract
This study explores a novel approach for non-intrusive stress assessment based on Heart Rate Variability (HRV) features extracted from Electrocardiogram (ECG) signals acquired through a toilet seat-embedded sensor system. Building upon previous work by our group, we developed a framework for real-time Stress Index (SI) computation and evaluated its physiological relevance by analyzing the correlation with classical HRV parameters (SDNN, RMSSD, and LF/HF ratio) across subjects with and without cardiovascular disease. Results showed moderate negative correlations between SI and both SDNN and RMSSD, and a positive correlation with LF/HF ratio, supporting the validity of SI as a marker of autonomic stress. The system successfully differentiated autonomic stress responses between groups, with pathological participants exhibiting higher SI values, likely influenced by underlying cardiac conditions and the inherently stressful hospital environment. In contrast, individuals in contexts without cardiovascular disease exhibited lower SI values, consistent with parasympathetic predominance. These findings highlight the potential of ambient physiological monitoring using everyday objects for continuous and passive health assessment. The system's unobtrusive nature and capability to detect subclinical autonomic dysregulation support its application in personalised digital health, stress prevention, and early detection of autonomic dysfunction.