Abstract
Accurate evaluation of forecasting models is essential for ensuring reliable predictions. Current practices for evaluating and comparing forecasting models focus on summarising performance into a single score, using metrics such as SMAPE. While convenient, averaging performance over all samples dilutes relevant information about model behaviour under varying conditions. This limitation is especially problematic for time series forecasting, where multiple layers of averaging-across time steps, horizons, and multiple time series in a dataset-can mask relevant performance variations. We address this limitation by proposing ModelRadar, a framework for evaluating univariate time series forecasting models across multiple aspects, such as stationarity, presence of anomalies, or forecasting horizons. We demonstrate the advantages of this framework by comparing 24 forecasting methods, including classical approaches and different machine learning algorithms. PatchTST, a state-of-the-art transformer-based neural network architecture, performs best overall but its superiority varies with forecasting conditions. For instance, concerning the forecasting horizon, we found that PatchTST (and also other neural networks) only outperforms classical approaches for multi-step ahead forecasting. Another relevant insight is that classical approaches such as ETS or Theta are notably more robust in the presence of anomalies. These and other findings highlight the importance of aspect-based model evaluation for both practitioners and researchers. ModelRadar is available as a Python package.

Abstract
The growth in cloud computing, Big Data, AI and high-performance computing (HPC) necessitate the deployment of additional data centers (DC's) with high energy demands. The unprecedented increase in the Thermal Design Power (TDP) of the computing chips will require innovative cooling techniques. Furthermore, DC's are increasingly limited in their ability to add powerful GPU servers by power capacity constraints. As cooling energy use accounts for up to 40% of DC energy consumption, creative cooling solutions are urgently needed to allow deployment of additional servers, enhance sustainability and increase energy efficiency of DC's. The information in this study is provided from Start Campus' Sines facility supported by Alfa Laval for the heat exchanger and CO2 emission calculations. The study evaluates the performance and sustainability impact of various data center cooling strategies including an air-only deployment and a subsequent hybrid air/water cooling solution all utilizing sea water as the cooling source. We evaluate scenarios from 3 MW to 15+1 MW of IT load in 3 MW increments which correspond to the size of heat exchangers used in the Start Campus' modular system design. This study also evaluates the CO2 emissions compared to a conventional chiller system for all the presented scenarios. Results indicate that the effective use of the sea water cooled system combined with liquid cooled systems improve the efficiency of the DC, plays a role in decreasing the CO2 emissions and supports in achieving sustainability goals. © 2013 IEEE.

Abstract
Hyper-parameter optimization in machine learning models is critical for achieving peak performance. Over the past few years, numerous researchers have worked on this optimization challenge. They primarily focused on batch learning tasks where data distributions remain relatively unchanged. However, addressing the properties of data streams poses a substantial challenge. With the rapid evolution of technology, the demand for sophisticated techniques to handle dynamic data streams is becoming increasingly urgent. This paper introduces a novel adaptation of the Fish School Search (FSS) Algorithm for online hyper-parameter optimization, the FSS-SPT. The FSS-SPT is a solution designed explicitly for the dynamic context of data streams. One fundamental property of the FSS-SPT is that it can change between exploration and exploitation modes to cope with the concept drift and converge to reasonable solutions. Our experiments on different datasets provide compelling evidence of the superior performance of our proposed methodology, the FSS-SPT. It outperformed existing algorithms in two machine learning tasks, demonstrating its potential for practical application.

Abstract
Underwater exploration relies heavily on autonomous underwater vehicles and sensor platforms for sustained monitoring of marine environments, yet their operational duration is limited by energy constraints. To enhance energy efficiency, various control strategies have been proposed, including robust, optimal, and disturbance-aware approaches. Recent work introduced a variable structure controller (VSC) with a constant-amplitude control action for depth control of a platform equipped with a variable buoyancy module, achieving an average 22% reduction in energy use in comparison with conventional PID-based controllers. In a separate paper, the conditions for its closed-loop stability were proven. This study extends these works by proposing a controller with a variable-amplitude control action designed to minimize energy consumption. A formal proof of stability is provided to guarantee safe operation even under conservative assumptions. The controller is applied to a previously developed depth-regulated sensor platform using a validated physical model. Additionally, this study analyzes how the controller parameters and mission requirements affect stability regions, offering practical guidelines for parameter tuning. A method to estimate oscillation amplitude during hovering tasks is also introduced. Simulation trials validate the proposed approach, showing energy savings of up to 16% when compared to the controller using a constant-amplitude control action.

Abstract
In recent years, non-residential buildings have increasingly adopted renewable energy generation systems to align with the European Union's goal of achieving carbon neutrality by 2050. However, energy storage systems play a fundamental role in maximising the use of the generated renewable energy. Due to their high acquisition costs, adequately sizing these systems is essential. Moreover, applying an optimal scheduling strategy for energy storage operation can significantly improve the economic viability of such systems by reducing energy-related costs. In this paper, a MILP-based optimisation algorithm-incorporating battery lifespan constraints-is applied to a reference commercial building to schedule the operation of the storage system. A sensitivity analysis on the installed photovoltaic power and energy storage capacity is performed to evaluate their impact on the economic and operational performance of the optimisation algorithm under different sizing configurations.

Abstract
Arterial stiffness (AS) is a well-established predictor of cardiovascular events, including myocardial infarction and stroke. One of the most recognized methods for assessing AS is through arterial pulse wave velocity (aPWV), which provides valuable clinical insights into vascular health. However, its measurement typically requires specialized equipment, making it inaccessible in primary healthcare centers and low-resource settings. In this study, we developed and validated different machine learning models to estimate aPWV using common clinical markers routinely collected in standard medical examinations. Thus, we trained five regression models: Linear Regression, Polynomial Regression (PR), Gradient Boosting Regression, Support Vector Regression, and Neural Networks (NNs) on the EVasCu dataset, a cohort of apparently healthy individuals. A 10-fold cross-validation demonstrated that PR and NN achieved the highest predictive performance, effectively capturing nonlinear relationships in the data. External validation on two independent datasets, VascuNET (a healthy population) and ExIC-FEp (a cohort of cardiopathic patients), confirmed the robustness of PR and NN (R- (2)> 0.90) across different vascular conditions. These results indicate that by using easily accessible clinical variables and AI-driven insights, it is possible to develop a cost-effective tool for aPWV estimation, enabling early cardiovascular risk stratification in underserved and rural areas where specialized AS measurement devices are unavailable.