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Funding Information: This work is funded by national funds through the FCT - Fundação para a Ciência e a Tecnologia , I.P., under the scope of the project DSAIPA/DS/0088/2019 and research and development units UNIDEMI (project UIDB/00667/2020 ) and NOVAMath (projects UIDB/00297/2020 and UIDP/00297/2020 ). Publisher Copyright: © 2023 The Author(s) Fires in urban areas typically carry severe consequences. High population density together with the complexity of urban network potentially imply significant impacts in property loss, physical damage and life losses. However, despite the impact that fires may have in urban areas, research in urban fire prediction remains limited. In this study, we modelled urban fire occurrences while making a comparative analysis of different strategies to account for spatial autocorrelation. Considering space dependence in addition to a range of social-economic explanatory variables has proven to strengthen the validity of the fitted models. The spatial Durbin error model, including population density, degraded buildings density and buying power, was selected as having the best fit. This model allowed to map the estimated probability of fire occurrence across Portugal, revealing a spatial pattern with clusters centred on the two main Portuguese city districts (Lisboa and Porto). Ultimately, the analysis of the relation between the observed urban fire incidence and the actual number of fire stations in each municipality allowed to underline the need for planning the spatial configuration of fire stations, both in number and location, at a regional scale. publishersversion published

In recent years, significant technological advances have emerged in renewable power generation systems (RPGS), making them more economical and competitive. On the other hand, for the RPGS to achieve the highest level of performance possible, it is important to ensure the healthy operation of their main building blocks. Power electronic converters (PEC), which are one of the main building blocks of RPGS, have some vulnerable components, such as capacitors, which are responsible for more than a quarter of the failures in these converters. Therefore, it is of paramount importance that the design of fault diagnosis techniques (FDT) assess the capacitor’s state of health so that it is possible to implement predictive and preventive maintenance plans in order to reduce unexpected stoppage of these systems. One of the most commonly used capacitors in power converters is the aluminum electrolytic capacitor (AEC) whose aging manifests itself through an increase in its equivalent series resistance (ESR). Several advanced intelligent techniques have been proposed for assessing AEC health status, many of which require the use of a current sensor in the capacitor branch. However, the introduction of a current sensor in the capacitor branch imposes practical restrictions; in addition, it introduces unwanted resistive and inductive effects. This paper presents an FDT based on the random forest classifier (RFC), which triggers an alert mechanism when the DC-link AEC reaches its ESR threshold value. The great advantage of the proposed solution is that it is non-invasive; therefore, it is not necessary to introduce any sensor inside the converter. The validation of the proposed FDT will be carried out using several computer simulations carried out in Matlab/Simulink.

Fundamental cosmology observations, such as the detection of the redshift drift and tests of the universality of physical laws, are key science and design drivers of the ArmazoNes high Dispersion Echelle Spectrograph (ANDES), an Extremely Large Telescope instrument. While separate forecasts for each of them have been reported, we have developed Fisher Matrix based forecast tools combining both of these observables. We demonstrate the synergies between the two ANDES datasets, quantifying the improvements in cosmology and fundamental physics parameter constraints for two separate theoretical paradigms. We publicly release this forecast code, which is one of the tools for the optimisation of the ANDES observing strategy., 8 pages, 3 figures, 2 tables, MNRAS (in press)

The QUBRICS (QUasars as BRIght beacons for Cosmology in the Southern hemisphere) survey aims at constructing a sample of the brightest quasars with z>~2.5, observable with facilities in the Southern Hemisphere. QUBRICS makes use of the available optical and IR wide-field surveys in the South and of Machine Learning techniques to produce thousands of bright quasar candidates of which only a few hundred have been confirmed with follow-up spectroscopy. Taking advantage of the recent Gaia Data Release 3, which contains 220 million low-resolution spectra, and of a newly developed spectral energy distribution fitting technique, designed to combine the photometric information with the Gaia spectroscopy, it has been possible to measure 1672 new secure redshifts of QUBRICS candidates, with a typical uncertainty $σ_z = 0.02$. This significant progress of QUBRICS brings it closer to (one of) its primary goals: providing a sample of bright quasars at redshift 2.5 < z < 5 to perform the Sandage test of the cosmological redshift drift. A Golden Sample of seven quasars is presented that makes it possible to carry out this experiment in about 1500 hours of observation in 25 years, using the ANDES spectrograph at the 39m ELT, a significant improvement with respect to previous estimates., 11 pages, 10 figures, accepted for publication in MNRAS

PURPOSE: To calculate unstratified and patient-specific meaningful improvement (MI) and patient acceptable symptom states (PASS) for the WOMAC total score in patients after total hip (THR) or total knee replacement (TKR). METHODS: A retrospective observational cohort study. Anchor-based receiver operator characteristics curves were used to estimate MI and PASS thresholds. RESULTS: Recovery paths were specific to individual characteristics of patients. An unstratified 12-months MI threshold of 28.1 (PASS: 13.3) and 17.8 (PASS: 15.8) for patients after THR and TKR, respectively, would unfairly detect critical recovery paths. CONCLUSIONS: Thresholds for treatment success need to be as patient-specific as possible.

Autonomous electric vehicles (AEVs) have garnered increasing attention in recent years as they hold significant promise for transforming the transportation sector. However, despite advances in the field, effective vehicle drive control remains a critical challenge that must be addressed to realize the full potential of AEVs. This study presents a novel approach to AEV drive control for concurrently generating a suitable speed profile and controlling the vehicle drive speed along a planned path that takes into account various driving circumstances that mimic real-world driving. The designed strategy is divided into two parts: The first part presents a proposed speed planning algorithm (SPA) that works on developing an adequate speed profile for vehicle navigation; first, the algorithm uses an approach for identifying sharp curves on the predefined trajectory; secondly, based on the dynamic properties of these curves, it generates an appropriate speed profile to ensure smooth vehicle travel across the entire trajectory with varying velocities. The second part proposes a new back-stepping control technique with a space vector modulation (SVM) strategy to control the speed of an induction motor (IM) as a traction part of the AEV. A load torque observer has been designed to enhance the speed-tracking task, while the system stability has been proven using Lyapunov theory. Through a series of experiments and simulations using MATLAB/Simulink software and the dSPACE 1104 real-time interface, we demonstrate the effectiveness of the SPA combined with the back-stepping control technique and highlight its potential to advance the field of AEV technology. Our findings have important implications for the design and implementation of AEVs and provide a foundation for future research in this exciting area of study.

Publisher Copyright: IEEE With growing interest in green technologies and sustainable transport, hybrid and pure electric powertrains are attracting a lot of attention. According to demanded metrics, such as lightweight and high-power density motors, these offer clear motivation to develop advanced thermal management methods and integrate them with conventional cooling approaches. The oil spray cooling method is one of the latest cooling methodologies integrated with the conventional cooling approaches. Therefore, this article provides a comprehensive study on the oil spray cooling approach for electrical motors utilized in electric vehicle (EV) traction application drives. The objective is to provide the reader with a picture of the state of the art regarding the opportunities, challenges, and offered solutions for the cooling requirements in EV motors. In addition, this article presents the guidelines for selecting the appropriate spray nozzles and coolants as well as estimating their performance, spray characteristics, and heat transfer coefficient (HTC) during the design period. Besides, various closed-loop systems of spray cooling approach, including refrigerants as a coolant, low viscosity liquid coolant with high boiling temperature, and high viscosity lubricant oil as a coolant, are discussed with the pros and cons of each system being compared. Moreover, real-life examples of this method, applied in the latest automotive traction motor prototypes, have been described and evaluated. Finally, the performance of different schemes is addressed.