Abstract

Abstract
The collection and annotation of data for supervised machine learning remain challenging and costly tasks, particularly in domains that demand expert knowledge. Depending on the application, labelling may require highly specialised professionals, significantly increasing the overall effort and expense. Active learning techniques offer a promising solution by reducing the number of annotations needed, thereby lowering costs without compromising model performance. This work proposes an active learning with a decreasing-budget-based strategy to reduce the effort required to annotate medical images. The strategy encourages data annotators to focus on initial iterations, optimise budget allocation, and ensure that the trained model achieves maximum performance with reduced effort in subsequent iterations. This strategy also improves the performance of deep learning models, which perform better with fewer images, reducing the specialists' workload. This work also introduces three experiments that contribute to understanding the impact of the strategy in the annotation process.

Abstract
The rapid growth of urban populations intensifies congestion, air pollution, and energy demand. Green mobility is central to sustainable smart cities, and the Internet of Things (IoT) offers a means to monitor, coordinate, and optimize transport systems in real time. This paper presents an Internet of Things (IoT)-based architecture integrating heterogeneous sensing with edge-cloud orchestration and AI-driven control for green routing and coordinated Electric Vehicle (EV) charging. The framework supports adaptive traffic management, energy-aware charging, and multimodal integration through standards-aware interfaces and auditable Key Performance Indicators (KPIs). We hypothesize that, relative to a static shortest-path baseline, the integrated green routing and EV-charging coordination reduce (H1) mean travel time per trip by >= 7%, (H2) CO2 intensity (g/km) by >= 6%, and (H3) station peak load by >= 20% under moderate-to-high demand conditions. These hypotheses are tested in Simulation of Urban MObility (SUMO) with Handbook Emission Factors for Road Transport (HBEFA) emission classes, using 10 independent random seeds and reporting means with 95% confidence intervals and formal significance testing. The results confirm the hypotheses: average travel time decreases by approximately 9.8%, CO2 intensity by approximately 8%, and peak load by approximately 25% under demand multipliers >= 1.2 and EV shares >= 20%. Gains are attenuated under light demand, where congestion effects are weaker. We further discuss scalability, interoperability, privacy/security, and the simulation-to-deployment gap, and outline priorities for reproducible field pilots. In summary, a pragmatic edge-cloud IoT stack has the potential to lower congestion, reduce per-kilometer emissions, and smooth charging demand, provided it is supported by reliable data integration, resilient edge services, and standards-compliant interoperability, thereby contributing to sustainable urban mobility in line with the objectives of SDG 11 (Sustainable Cities and Communities).

Abstract
The HyStorization project aims to advance the modelling and operational understanding of hybrid electrochemical energy storage systems, focusing on Lithium-ion (Li-ion) and Vanadium Redox Flow Batteries (VRFBs). These technologies are key enablers of flexible, reliable, and scalable grid-scale energy storage. While Li-ion batteries are well-established for high-power applications, VRFBs offer promising advantages for medium- to long-duration storage due to their durability and decoupled energy and power capacities. The primary objective is to develop linearized battery models for both technologies, derived from experimental data, that accurately capture efficiency and power limits as functions of the State of Charge (SoC). These models are intended for integration into Mixed-Integer Linear Programming (MILP) tools to optimize energy dispatch in hybrid storage systems. A comprehensive testing campaign was conducted on three BYD stationary Li-ion battery systems. Due to a malfunction in one unit, the remaining three—of similar age and usage—were treated as a single representative system. A Python-based controller was developed to automate cycling and collect high-resolution data (1-second intervals) via HTTP. The testing protocol included: • Constant power cycles for initial validation and degradation screening. • Constant current cycles for parameter extraction. Key findings include: • A slight but consistent improvement in SoC estimation accuracy using a linear model over a bucket model (~2% reduction in MAE and MSE). • Shorter resampling intervals (e.g., 1-minute vs. 15-minute) improved accuracy, but the most significant reduction in error came from refreshing the SoC with real measurements rather than relying on estimated values. • SoC limits, while useful for safety, were found to be overly restrictive and may not reflect the battery’s full operational flexibility. • Attempts to assess cyclic degradation were inconclusive due to the limited number of cycles and short observation window. The final linear model includes parameters for nominal charge/discharge voltages, inverter efficiencies, and dynamic SoC limits as functions of DC power. These were validated against real operational data and compared with manufacturer-based models. Concerning the VRFB, the project originally planned to conduct targeted tests on the VRFB to: • Evaluate energy efficiency across different SoC levels and operational ranges. • Determine maximum and minimum effective power ratings as functions of SoC. • Support the development of non-linear models that will be linearized for MILP integration. However, due to a malfunction, the VRFB could not be tested as planned. Instead, the projectrelied on previously collected characterization data, which did not fully cover the intended test scope. Despite these limitations, the available data was used to: • Analyse energy efficiency trends across selected states of charge (SoC) and operational conditions. • Estimate effective power ratings within the constraints of the existing dataset. • Support the preliminary development of non-linear models, with the aim of future linearization for MILP integration. While these efforts provided valuable insights, the absence of new experimental data limited the ability to fully capture the unique operational characteristics of VRFBs, such as their decoupled energy and power capabilities and their suitability for long-duration storage. The project is expected to deliver: • Validated, MILP-compatible models for both Li-ion and VRFB technologies. • Enhanced dispatch strategies for hybrid storage systems. • Improved integration of real-time SoC measurements to reduce estimation error. • Recommendations for longer-term testing to better assess degradation and refine model accuracy. In conclusion, the HyStorization project provides a foundational step toward more accurate, data-driven modelling of hybrid storage systems. It highlights the importance of real-time data, flexible modelling approaches, and the need for continued testing to support the evolving role of batteries in grid operations.

Abstract

A novel technique based on multiple amplitude wavelength modulation spectroscopy (MA-WMS) for simultaneous measurement of CH4 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 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 a few percentage points up to 50% and pressure from 0.02 atm up to 2 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 work introduces a web application for extracting, processing, and visualizing data from sleep studies reports. Using Optical Character Recognition (OCR) and Natural Language Processing (NLP), the pipeline extracts over 75 key data points from four types of sleep reports. The web application offers an intuitive interface to view individual reports' details and aggregate data from multiple reports. The pipeline demonstrated 100% accuracy in extracting targeted information from a test set of 40 reports, even in cases with missing data or formatting inconsistencies. The developed tool streamlines the analysis of OSA reports, reducing the need for technical expertise and enabling healthcare providers and researchers to utilize sleep study data efficiently. Future work aims to expand the dataset for more complex analyses and imputation techniques.