Artificial intelligence (AI) is increasingly applied in a wide range of healthcare and Intensive Care Unit (ICU) areas to serve—among others—as a tool for disease detection and prediction, as well as for healthcare resources’ management. Since sepsis is a high mortality and rapidly developing organ dysfunction disease afflicting millions in ICUs and costing huge amounts to treat, the area can benefit from the use of AI tools for early and informed diagnosis and antibiotic administration. Additionally, resource allocation plays a crucial role when patient flow is increased, and resources are limited. At the same time, sensitive data use raises the need for ethical guidelines and reflective datasets. Additionally, explainable AI is applied to handle AI opaqueness. This study aims to present existing clinical approaches for infection assessment in terms of scoring systems and diagnostic biomarkers, along with their limitations, and an extensive overview of AI applications in healthcare and ICUs in terms of (a) sepsis detection/prediction and sepsis mortality prediction, (b) length of ICU/hospital stay prediction, and (c) ICU admission/hospitalization prediction after Emergency Department admission, each constituting an important factor towards either prompt interventions and improved patient wellbeing or efficient resource management. Challenges of AI applications in ICU are addressed, along with useful recommendations to mitigate them. Explainable AI applications in ICU are described, and their value in validating, and translating predictions in the clinical setting is highlighted. The most important findings and future directions including multimodal data use and Transformer-based models are discussed. The goal is to make research in AI advances in ICU and particularly sepsis prediction more accessible and provide useful directions on future work.

In the world of transportation, the ceaseless evolution of Information and Communication Technologies (ICT) is reflected on their migration towards In-Vehicle-Infotainment (IVI) systems, which are characterized by innovative applications and services. Such systems aim to support drivers/passengers with a varying set of functions, enhancing the quality of driving through entertainment. The goal of this study is to introduce a novel IVI cognitive functionality that automatically and dynamically proposes the optimal music genre to the drivers/users when they want to make a certain journey with their owned (or shared) vehicles. The proposed recommender functionality utilizes in an automated manner (i) drivers/users' profile data and current situation, (ii) drivers/users' personal preferences, (iii) external environment information obtained from sensor measurements, and (iv) previous knowledge and experience. Knowledge is obtained through the exploitation of Bayesian networking principles in combination with a practical implementation of the Naive-Bayes model. Indicative simulation results showcase the efficiency of the proposed infotainment functionality, in terms of accuracy and speed of convergence, in proactively identifying the optimal music genre and accordingly notifying the drivers/users.

The motion of pedestrians is a paradigmatic phenomenon to study collective human behavior. We propose a model-free approach to analyze the movement of pedestrians in experiments and get a quantitative understanding of crowd dynamics. Using concepts from control and analysis of dynamical systems, we set up a scheme which allows us to identify dynamical unstable signatures in pedestrian flows. These signatures are the building blocks for crowd control and soft management of people and thus result in a fundamental understanding of collective human behavior. Our approach is entirely data driven, and we provide a proof of concept using field and laboratory experiments. In addition, this methodology provides, based on experimental observations, quantitative benchmarks to judge the quality of mathematical models for pedestrian motion.

This paper presents a framework to perform bifurcation analysis in laboratory experiments or simulations. We employ control-based continuation to study the dynamics of a macroscopic variable of a microscopically defined model, exploring the potential viability of the underlying feedback control techniques in an experiment. In contrast to previous experimental studies that used iterative root-finding methods on the feedback control targets, we propose a feedback control law that is inherently noninvasive. That is, the control discovers the location of equilibria and stabilizes them simultaneously. We call the proposed control zero-in-equilibrium feedback control and we prove that it is able to stabilize branches of equilibria, except at singularities of codimension \(n+1\) , where \(n\) is the number of state space dimensions the feedback can depend on. We apply the method to a simulated evacuation scenario where pedestrians have to reach an exit after maneuvering left or right around an obstacle. The scenario shows a hysteresis phenomenon with bistability and tipping between two possible steady pedestrian flows in microscopic simulations. We demonstrate for the evacuation scenario that the proposed control law is able to uniformly discover and stabilize steady flows along the entire branch, including points where other noninvasive approaches to feedback control become singular.

The state of road surfaces can have a significant impact on vehicle handling, passenger comfort, safety, fuel consumption, and maintenance requirements. For this reason, it is important to analyze road conditions in order to improve traffic safety, optimize fuel efficiency, and provide a smoother travel experience. This research presents a federated learning analysis that brings together edge computing and cloud technology, by identifying various road conditions through a multi-label road surface classification analysis. The presented analysis prioritizes the privacy of road users’ data and leverages the advantages of collective data analysis while building confidence in the system. Multi-label classification is applied in order to capture complexity by assigning multiple relevant labels, thus providing a richer and more detailed understanding of the road conditions. According to the experiments, this approach efficient classifies road surface images, achieving comprehensive coverage even in scenarios where data from certain edges is limited.

Highly automated passenger vehicles hold great potential to alleviate traffic congestion, enhance road safety, and revolutionize the travel journey. However, while much attention has been given to the technical aspects of this technology, the investigation of public acceptance remains crucial for successful implementation in the global market. To address this gap, this paper introduces innovative research that explores the predictors influencing consumers’ intention to adopt highly automated passenger vehicles. Through an online questionnaire-based survey conducted among European adults, we extend the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to incorporate three additional constructs: perceived reliability/trust, perceived financial cost, and perceived driving enjoyment. The key findings of this study underscore the significance of driving enjoyment, financial cost, social influences, and reliability/trust as influential predictors of consumers’ intention to adopt highly automated passenger vehicles. By considering these factors, automotive stakeholders can gain valuable insights to develop effective strategies and approaches for the successful implementation of highly automated passenger vehicles in the near future. Last, its innovations pave the way for a transformative shift in transportation, enabling the realization of safer, more efficient, and enjoyable travel experiences for individuals and society as a whole.