Abstract
The PASSPort project develops machine learning-based systems to conduct risk analyses, implement nowcasting and forecasting of weather and climate conditions at the port scale, and predict the impacts of potential oil spills. Furthermore, the project defines predictive monitoring systems for port infrastructures, using sensor-based control systems to ensure operational safety. It also analyzes the impact of maritime traffic on urban mobility, aiming to promote the sustainability and safety in port-city interface areas.
Partner
Università degli Studi di Catania – Università degli Studi di Messina
Abstract
The IP-SIM project focuses on improving the use and integration of existing sensors: particularly standard surveillance cameras already widely deployed in ports, which have the potential to provide highly valuable information. Specifically, the project aims to develop a sub-system easily integrable into existing technological architectures, providing intelligent monitoring functions such as detection and tracking of vessels and people. To this end, the project explores advanced machine learning techniques and defines an accelerated testing/validation phase, supported by publicly available databases and video stream sources suitable for the purpose.
Partner
Università degli Studi di Sassari
Abstract
The H-PORT project aims to promote and integrate hydrogen technologies and advanced modeling and simulation systems to address pressing challenges in the port sector, including emissions reduction, operational inefficiencies, and safety concerns.
The project is structured into three main phases: the first focuses on design innovation, the second on the development of a digital twin model and simulator, and the third on regulatory and economic analysis. H-PORT will leverage advanced energy systems and unmanned operational systems along the port logistics chain, to enhanced safety and sustainability adopting a multi-capital approach.
Partner
Università degli Studi della Calabria
Abstract
The HINSPIRATION project aims to create a hybrid, hydrogen-ready smart grid in the port of the island of Carloforte, Sardinia. The design and simulation of the hydrogen system—powered by a sophisticated combination of advanced algorithms, analysis software, and widespread sensors—will enable monitoring and forecasting of the renewable hydrogen ecosystem, supporting islands and ports seeking in accelerating the adoption of innovative and sustainable technologies.
Partner
Comune di Carloforte
Abstract
The AI-PORT project proposes the development of an intelligent system for localization and optimization of port logistics operations, based on computer vision, machine learning, and the Internet of Things. The goal is to reduce vehicle dwell time in container terminals by automatically identifying trailers and generating real-time optimized routes.
Partner
Università degli Studi di Napoli Parthenope
Abstract
AIPAG is a project aimed at transforming the management of road access gates at the port through cutting-edge technological solutions. The main goal is to automate and digitalize vehicle access controls, reducing operational times, enhancing personnel safety, and minimizing errors associated with manual management.
By leveraging a combination of artificial intelligence, IoT sensors, smart cameras, and laser scanners, AIPAG introduces a platform capable of acquiring real-time data, reading license plates and identification codes, detecting damage and hazards, and integrating with existing systems.
Partner
Autorità di Sistema Portuale dei mari Tirreno Meridionale e Ionio
Abstract
Alzheimer’s disease (AD) is strongly influenced by cardiometabolic risk factors. The RICAMI project involves a prospective study on 80 subjects undergoing a 6-month anti-inflammatory nutritional treatment.
The project aims to validate cardiometabolic, morphological, and vascular neuroradiological risk markers using MRI in patients with Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD). At the same time, it intends to develop new AI algorithms capable of assessing brain trophism and analyzing changes in cerebral blood flow before and after the adoption of an anti-inflammatory diet. In addition, the research focuses on designing advanced AI tools for early diagnosis and improved prognosis of Alzheimer’s Disease, through the integration of the data collected.
Partner
Università degli Studi di Palermo
Abstract
Supporting patients with cognitive impairments requires effective and unobtrusive monitoring systems. The STOPme project focuses on a paradigmatic case: Rett syndrome, a rare neurodevelopmental disorder characterized by severe motor and cognitive impairment, including the presence of stereotypies and cardiorespiratory dysfunctions.
STOPme aims to evaluate in real-time the patient’s psychomotor activation state, monitoring the onset of neuromotor stereotypies and hypo-/hyperventilation patterns, in order to train the patient to interrupt the stereotypies.
Partner
Università degli Studi di Cagliari – Istituto Universitario Studi Superiori
Abstract
The project focuses on the development of a decision support system to facilitate the early diagnosis and monitoring of pediatric patients with posterior urethral valves (PUV).
The system will integrate clinical, anthropometric, medical history, and instrumental data through multimodal analysis, enabling accurate and timely predictions, prior to surgery, of which patients may have PUV and which may be at increased risk of developing chronic kidney disease during follow-up.
Partner
Università degli Studi della Campania “Luigi Vanvitelli”
Abstract
The project aims to optimize clinical routines in obstetrics through the use of a high-end ultrasound platform, ideal for research, diagnosis, and the study of maternal-fetal prenatal conditions, combined with an additional computational unit. The goal is to enhance decision-making processes in care and assistance, tailored to the complexity of each case as identified early in the clinical pathway.
Partner
Università degli Studi di Bari Aldo Moro
