Abstract
The gold standard real-time core temperature (CT) monitoring methods are invasive and cost-inefficient. The application of the Kalman filter for an indirect estimation of CT has been explored in the literature for more than 10 years. This paper presents a comparative study between different state-of-the-art Extended Kalman Filter (EKF) approaches. Moreover, we proposed the addition of an extra layer to the pipeline that applies a pre-emptive mapping concept based on the physiological response of the heart rate (HR) signal, before using it as input to the EKF. The algorithm was trained and tested using two datasets (18 subjects). The best-performing approach with the novel pre-emptive mapping achieved an average Root Mean Squared Error (RMSE) of 0.34 ^?C, while without pre-emptive mapping, it resulted in an RMSE of 0.41 ^?C, leading to a performance improvement of 17%. Given these favorable outcomes, it is compelling to assess the efficacy of this method on a larger dataset in the future.

Abstract
Identifying influential spreaders in temporal networks is crucial for understanding and controlling the dynamics of spreading. However, existing methods, such as temporal betweenness, closeness, pagerank, degree, and local path-based centrality, face several limitations, including high computational complexity, reliance on shortest paths, convergence issues, inability to capture influence dynamics with insufficient neighboring nodes, and a primary focus on local structural information. This paper presents PathSAGE, a novel method that addresses these problems. It integrates GraphSAGE, a deep learning model, to capture global node information while incorporating temporal local path counts as a key feature. Unlike other global feature-capturing methods, PathSAGE optimises computational complexity. Experimental results on thirteen real-world temporal networks demonstrate that PathSAGE outperforms the state-of-the-art methods in accurately identifying influential spreaders. PathSAGE exhibits a strong correlation with the Temporal Susceptible-Infected-Recovered (TSIR) model and achieves a relative improvement percentage (eta%) ranging from 0.12% to 70.70%. Additionally, PathSAGE attains the lowest average robustness value of 0.17, highlighting its effectiveness in identifying influential spreaders within temporal networks.

Abstract
The growing integration of renewable energy sources and the widespread electrification of the energy demand have significantly reduced the capacity margin of the electrical grid. This demands a more flexible approach to grid operation, for instance, combining real-time topology optimization and redispatching. Traditional expert-driven decision-making rules may become insufficient to manage the increasing complexity of real-time grid operations and derive remedial actions under the N-1 contingency. This work proposes a novel hybrid AI framework for power grid topology control that integrates genetic network programming (GNP), reinforcement learning, and decision trees. A new variant of GNP is introduced that is capable of evolving the decision-making rules by learning from data in a reinforcement learning framework. The graph-based evolutionary structure of GNP and decision trees enables transparent, traceable reasoning. The proposed method outperforms both a baseline expert system and a state-of-the-art deep reinforcement learning agent on the IEEE 118-bus system, achieving up to an 28% improvement in a key performance metric used in the Learning to Run a Power Network (L2RPN) competition.

Abstract
Eye diseases can have highly adverse outcomes without an early diagnosis and correct monitoring. Retinopathy of Prematurity (ROP) Plus Form, in particular, is a disease that can lead to childhood blindness, and its diagnosis requires medical experts to examine the retinal condition manually. Although developments in screening equipment have helped, this is still a time-consuming and subjective task. The development of automatic tools for Retinal Blood Vessel Segmentation allows the extraction of blood vessels from fundus images, which healthcare experts can then use to perform the diagnosis, monitoring, and prognosis of eye diseases. Thus, developing such a segmentation tool is a widely explored task with different methodologies that can be followed. However, many studies try to segment all the blood vessels rather than only the most important ones. In this work, we present a segmentation pipeline to segment only the main vessels whose characteristics can be used to assess ROP Plus Form disease. This pipeline uses different operations and filters, including CIELAB Enhancement, Background Normalization, Bell-Shaped Gaussian Matched Filtering, Modified Top-Hat operation, and Frangi Filtering. The final segmentation is done by determining a threshold value using the Triangle Threshold algorithm. The pipeline was tested in the well-known DRIVE Database, achieving an Accuracy of 0.949, Specificity of 0.963, and Sensitivity of 0.756. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
A novel technique based on multiple amplitude wavelength modulation spectroscopy (MA-WMS) for simultaneous measurement of CH4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {CH}_4$$\end{document} gas concentration and pressure was developed and validated both through simulation and experiment, showing good agreement. To capture the spectrum broadening caused by increasing pressure and concomitantly obtain the concentration at the sensor's location, a laser centered at 1650.9 nm was subjected to multiple amplitude modulation depths while the 2fm\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2f_{m}$$\end{document} signal, normalized by the DC component (an invariant quantity under optical loss), was recorded. While the use of a single and fixed modulation can introduce an ambiguity, as different pairs of pressure and concentration can yield the same value, this ambiguity is eliminated by employing multiple amplitude modulations. In this approach, the intersection point of the three level curves can provide the local pressure and concentration. The proposed system was able to measure concentrations from 5% up to 45% and pressures from 0.25 atm up to 1.75 atm, with a maximum error of 2% in concentration and 0.06 atm in pressure, respectively. The system was also tested for attenuation insensitivity, demonstrating that measurements were not significantly affected for up to 10 dB applied optical loss.

Abstract
This chapter provides an overview of the development Phases involved in transforming a technology originated in research into a medical product for commercialization. It first describes the four main Phases, from the emergence of the need for the product to its post-marketing obligations. It is intended to help the interested reader understand the stages, documents, guidelines, and regulations that a medical device must go through in order to be marketed. Special highlight is given to the necessary topics that must be addressed in order for the device to be certified. Every product that goes to market must be certified by some regulatory body in order to ensure that it will not cause any negative impact on its users. Further, for medical devices, these requirements are heightened, as they may come in contact with the user, potentially causing a direct risk to them. Thus, reading this chapter will provide the reader with an understanding of these Phases within the industrial environment as well as the aspects that must be taken into account before placing a medical device on the market. © Springer Nature Switzerland AG 2026.