Abstract
Nowadays, health systems comprise a series of resources structured to provide healthcare services to meet our health needs. However, premature deaths still occur. To quantify and understand personal healthcare conditions affecting such amenable mortality, the Healthcare Access and Quality Index (HAQI) was put forward, evaluating 195 countries and territories since 1990. Nevertheless, the literature acknowledges a series of limitations of this framework, such as the drawbacks of using principal component analysis to aggregate individual indicators, the absence of control for financing and environmental conditions, and the presence of a substantial degree of data uncertainty. Accordingly, we propose a methodological alternative to the computation of the HAQI using a novel fuzzy Data Envelopment Analysis model to handle the aforementioned shortcomings. We also propose its extension towards the quantification of efficiency (E-HAQI) - in the sense of value for money - by incorporating financial aspects as modelling inputs. This way, we contribute with innovative modelling approaches that can also deal with the high degree of data uncertainty. Furthermore, in a second -stage analysis, the impact of key exogenous factors on healthcare access and quality is assessed via non -parametric hypothesis testing. Our results show positive and significant correlations of both the revisited HAQI and E-HAQI with the original HAQI 2016 dataset. They also reveal a better use of resources by European and Oceanian countries and territories than by Sub-Saharan African ones. Concerning contextual determinants, socio-demographic development, human development, and the type of health system were found to be statistically significant drivers of healthcare access and quality efficiency.

Abstract
[No abstract available]

Abstract

Abstract
The GNAO facility is an upcoming adaptive optics (AO) system for the Gemini North Telescope. It will deliver both wide and narrow field AO capabilities to its first light instrument GIRMOS. GIRMOS is a multi-object AO (MOAO) instrument that houses four near infrared (NIR) IFU spectrographs and a NIR imager similar to GSAOI at Gemini South. The required sensitivity of the combined system is largely driven by rapid transient followup AO-corrected Imaging and the required sensitivity is in part driven by the performance of the AO system. Up until recently, the estimated AO performance feeding the combined GNAO+GIRMOS imaging system was derived from models using limited information on what the actual parameters will eventually be. However, the AO system (currently called the AO Bench, or AOB) recently underwent a competitive bidding process to derive an AO design that met or exceeded our AO requirements. This work summarizes the update to the combined GNAO+GIRMOS imaging system performance based on the newly designed AOB parameters. We discuss the impact due to the changes in performance, specifically with respect to key science cases of the GNAO+GIRMOS imaging system compared to the previous models of the AO system. We also discuss the largest hurdles in terms of parameters that affect performance, such as telescope vibrations and detector quantum efficiency and our plans for mitigation.

Abstract
Unmanned Aerial Vehicles (UAVs) are increasingly used as wireless communications nodes, serving as Wi-Fi Access Points and Cellular Base Stations. To enable energy-efficient access networks, we previously introduced the Sustainable multi-UAV Performance-aware Placement (SUPPLY) algorithm, which focuses on the energy-efficient placement of UAVs as Flying Access Points (FAPs) to serve Ground Users (GUs). However, SUPPLY did not address the backhaul link. This paper presents the Simple Gateway Positioning (SGWP) solution, which optimizes the position of a Gateway (GW) UAV to ensure backhaul connectivity in a two-tier network. We integrate SUPPLY for FAP positioning with SGWP for GW placement and evaluate their combined performance under various scenarios involving different GUs' Quality of Service (QoS) requirements and positions. Our results demonstrate that SUPPLY and SGWP can be used jointly in a two-tier network with minimal performance degradation.

Abstract
The emergence of Digital Twins (DT) in Industry 4.0 has enabled the decision support systems taking advantage of more effective recommendation systems (RS). Despite the RS's growing popularity and ability to support decision-makers, these face two significant challenges, cold-start and data sparsity, which limits the system's capability to provide effective and accurate decision support. This paper aims to address these issues by conducting a literature review, analysing the current research landscape, and identifying the main enabling methods, algorithms, and similarity measures to mitigate these challenges. The performed analysis enables the point out of future research directions for developing effective and accurate RS that empower decision-makers.