Abstract
Health system performance assessment (HSPA) is essential for health planning and to improve population health. One of the HSPA domains is related to effectiveness, which can be represented considering different dimensions. Composite indicators can be used to summarize complex constructs involving several indicators. One example of such efforts is the Healthcare Access and Quality Index from the Global Burden of Diseases Study, in which different causes of mortality amenable to health care are summarized in this index through principal component analysis and exploratory factor analysis. While these approaches use the variance of the indicators, marginal improvement is not considered, that is, the distance to the best practice frontier. In this study we propose an innovative benefit-of-the-doubt approach to combine frontier analysis and composite indicators, using amenable mortality estimates for 188 countries. In particular, we include flexible aggregating weighting schemes and a robust and conditional approach. The dual formulation gives information on the peers and the potential mortality rate reduction targets considering the background conditions. In absolute terms, Andorra and high-income countries are the most effective regarding healthcare access and quality, while sub-Saharan African and South Asian countries are the least effective. North African and Middle Eastern countries benefit the most when epidemiological patterns, geographical proximity, and country development status are considered.

Abstract
This chapter aims to analyze and map the behaviors and strategies employed by organizations recognized for their innovation, with the goal of developing a comprehensive innovation management framework. This framework is designed to merge innovation practices with elements of traditional management, creating a hybrid model to support companies, universities, and research institutes in fostering innovation. Rooted in an understanding of human evolution, the framework will reflect changes in needs, skills, and behaviors over time, enabling institutions to adapt their innovation strategies to align with societal and individual development. Adopting an interdisciplinary approach, it will integrate concepts from innovation, organizational management, and the human sciences to establish a structure that supports sustainable innovation while addressing contemporary challenges. © 2025, IGI Global Scientific Publishing.

Abstract
In this study, we demonstrate the measurement of electric power using an optical ground wire ( OPGW). The tests were conducted on an OPGW cable from a high-voltage transmission line in Sines, Portugal, operating at 400 kV. A buried fiber position, free of 50 Hz and 100 Hz frequency interference, was selected to confirm that the 50 Hz frequency is not due to mechanical perturbation or electronic noise. Additionally, two suspended fiber positions (at 2500 m and 8500 m), where these frequencies were clearly observed, were analyzed. This study also examined the positioning of poles and splice detection between cables.

Abstract
With the consistent adoption of iVR and growing research on the topic, it becomes fundamental to understand how the perception of Realism plays a role in the potential of iVR. This work puts forwards a hypothesis-driven theoretical model of how the perception of each multisensory stimulus (Visual, Audio, Haptic and Scent) is related to the perception of Realism of the whole experience (Subjective Realism) and, in turn, how this Subjective Realism is related to Involvement and Presence. The model was validated using a sample of 216 subjects in a multisensory iVR experience. The results indicated a good model fit and provided evidence on how the perception of Realism of Visual, Audio and Scent individually is linked to Subjective Realism. Furthermore, the results demonstrate strong evidence that Subjective Realism is strongly associated with Involvement and Presence. These results put forwards a validated questionnaire for the perception of Realism of different aspects of the virtual experience and a robust theoretical model on the interconnections of these constructs. We provide empirical evidence that can be used to optimise iVR systems for Presence, Involvement and Subjective Realism, thereby enhancing the effectiveness of iVR experiences and opening new research avenues.

Abstract
The Internet of Vehicles (IoV) presents complex cybersecurity challenges, particularly against Denial-of-Service (DoS) and spoofing attacks targeting the Controller Area Network (CAN) bus. This study leverages the CICIoV2024 dataset, comprising six distinct classes of benign traffic and various types of attacks, to evaluate advanced machine learning techniques for instrusion detection systems (IDS). The models XGBoost, Random Forest, AdaBoost, Extra Trees, Logistic Regression, and Deep Neural Network were tested under realistic, imbalanced data conditions, ensuring that the evaluation reflects real-world scenarios where benign traffic dominates. Using hyperparameter optimization with Optuna, we achieved significant improvements in detection accuracy and robustness. Ensemble methods such as XGBoost and Random Forest consistently demonstrated superior performance, achieving perfect accuracy and macro-average F1-scores, even when detecting minority attack classes, in contrast to previous results for the CICIoV2024 dataset. The integration of optimized hyperparameter tuning and a broader methodological scope culminated in an IDS framework capable of addressing diverse attack scenarios with exceptional precision.

Abstract
Autonomous Vehicles aim to understand their surrounding environment by detecting relevant objects in the scene, which can be performed using a combination of sensors. The accurate prediction of pedestrians is a particularly challenging task, since the existing algorithms have more difficulty detecting small objects. This work studies and addresses this often overlooked problem by proposing Multimodal PointPillars (M-PP), a fast and effective novel fusion architecture for 3D object detection. Inspired by both MVX-Net and PointPillars, image features from a 2D CNN-based feature map are fused with the 3D point cloud in an early fusion architecture. By changing the heavy 3D convolutions of MVX-Net to a set of convolutional layers in 2D space, along with combining LiDAR and image information at an early stage, M-PP considerably improves inference time over the baseline, running at 28.49 Hz. It achieves inference speeds suitable for real-world applications while keeping the high performance of multimodal approaches. Extensive experiments show that our proposed architecture outperforms both MVX-Net and PointPillars for the pedestrian class in the KITTI 3D object detection dataset, with 62.78% in $AP_{BEV}$ (moderate difficulty), while also outperforming MVX-Net in the nuScenes dataset. Moreover, experiments were conducted to measure the detection performance based on object distance. The performance of M-PP surpassed other methods in pedestrian detection at any distance, particularly for faraway objects (more than 30 meters). Qualitative analysis shows that M-PP visibly outperformed MVX-Net for pedestrians and cyclists, while simultaneously making accurate predictions of cars.