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Optimizing vessel hull resistance is pivotal for enhancing maritime performance and minimizing environmental impacts. Traditional methods combine expert intuition with Data-Driven Models (DDMs), relying on parametrization to predict and optimize hull geometries using Experimental Fluid Dynamics (EFD) or Computational Fluid Dynamics (CFD) data. However, these conventional approaches are hampered by several limitations: they require significant human input, are computationally intensive and costly, and lack flexibility in adapting to new families of geometries or parameters beyond predefined ranges. Addressing these challenges, our research introduces a novel method that significantly reduces the need for human intervention, computational resources, and costs, while also improving the model’s adaptability. By proposing a new a parametrization technique that accurately encompasses the Delft Systematic Yacht Hull Series (DSYHS), we demonstrate that DDMs can be effectively trained directly on EFD datasets. This eliminates the dependency on extensive CFD simulations or the generation of new EFD data tailored to a specific investigation. Our approach matches the performance of leading-edge CFD models, even in extrapolating conditions, with physical plausibility and minimal human oversight. The validation of our method under various and increasingly complex extrapolating scenarios, employing statistical analyses on the DSYHS EFD dataset and comparisons with state-of-the-art CFD models, underscores the effectiveness of our proposal. Furthermore, we demonstrated that our model can successfully optimize hull resistance when navigating geometric parameters outside the confines of the DSYHS validating our results through leading-edge CFD simulations. This work addresses the limitations of existing methodologies by offering a novel approach more accurate, efficient, cost-effective, flexible, automated, and robust to extrapolation for hull resistance optimization.

Due to increasing environmental concerns and global energy demand, the development of Floating Offshore Wind Turbines (FOWTs) is on the rise. FOWTs offer a promising solution to expand wind farm deployment into deeper waters with abundant wind resources. However, their harsh operating conditions and lower maturity level compared to fixed structures pose significant engineering challenges, notably in the design phase. A critical challenge is the time-consuming hydromechanics analysis traditionally done using computationally intensive Computational Fluid Dynamics (CFD) models. In this study, we introduce Artificial Intelligence-based surrogate models using state-of-the-art Machine Learning algorithms. These surrogate models achieve CFD-level accuracy (within 3% difference) while dramatically reducing computational requirements from minutes to milliseconds. Specifically, we build a surrogate model for characterizing the hydrodynamic response of a floating spar-type offshore wind turbine (including added mass, radiation damping matrices, and hydrodynamic excitation) using computationally efficient shallow Machine Learning models, optimizing the trade-off between computational efficiency and accuracy, based on data generated by a cutting-edge potential-flow code.

Current rail traffic management and control systems cannot be easily upgraded to the new needs and challenges of modern railway systems because they do not offer interoperable data structures and standardized communication interfaces. To meet this need, the Horizon 2020 Shift2Rail OPTIMA project has developed a communication platform for testing and validating the new generation of traffic management systems (TMS), whose main innovative features are the interoperability of the data structures used, standardization of communications, continuous access to real-time and persistent data from heterogeneous data sources, modularity of components and scalability of the platform. This paper presents the main components, their functions and characteristics, then describes the testing and validation of the platform, even when federated with other innovative TMS modules developed in separate projects. The successful validation of the system has confirmed the achievement of the objectives set and allowed a new set of objectives to be defined for the reference platform for the railway TMS/Traffic Control systems.

Applying computational statistics or machine learning methods to data is a key component of many scientific studies, in any field, but alone might not be sufficient to generate robust and reliable outcomes and results. Before applying any discovery method, preprocessing steps are necessary to prepare the data to the computational analysis. In this framework, data cleaning and feature engineering are key pillars of any scientific study involving data analysis and that should be adequately designed and performed since the first phases of the project. We call "feature" a variable describing a particular trait of a person or an observation, recorded usually as a column in a dataset. Even if pivotal, these data cleaning and feature engineering steps sometimes are done poorly or inefficiently, especially by beginners and unexperienced researchers. For this reason, we propose here our quick tips for data cleaning and feature engineering on how to carry out these important preprocessing steps correctly avoiding common mistakes and pitfalls. Although we designed these guidelines with bioinformatics and health informatics scenarios in mind, we believe they can more in general be applied to any scientific area. We therefore target these guidelines to any researcher or practitioners wanting to perform data cleaning or feature engineering. We believe our simple recommendations can help researchers and scholars perform better computational analyses that can lead, in turn, to more solid outcomes and more reliable discoveries.

Abstract Background Sepsis is a life-threatening clinical condition that happens when the patient’s body has an excessive reaction to an infection, and should be treated in one hour. Due to the urgency of sepsis, doctors and physicians often do not have enough time to perform laboratory tests and analyses to help them forecast the consequences of the sepsis episode. In this context, machine learning can provide a fast computational prediction of sepsis severity, patient survival, and sequential organ failure by just analyzing the electronic health records of the patients. Also, machine learning can be employed to understand which features in the medical records are more predictive of sepsis severity, of patient survival, and of sequential organ failure in a fast and non-invasive way. Dataset and methods In this study, we analyzed a dataset of electronic health records of 364 patients collected between 2014 and 2016. The medical record of each patient has 29 clinical features, and includes a binary value for survival, a binary value for septic shock, and a numerical value for the sequential organ failure assessment (SOFA) score. We disjointly utilized each of these three factors as an independent target, and employed several machine learning methods to predict it (binary classifiers for survival and septic shock, and regression analysis for the SOFA score). Afterwards, we used a data mining approach to identify the most important dataset features in relation to each of the three targets separately, and compared these results with the results achieved through a standard biostatistics approach. Results and conclusions Our results showed that machine learning can be employed efficiently to predict septic shock, SOFA score, and survival of patients diagnoses with sepsis, from their electronic health records data. And regarding clinical feature ranking, our results showed that Random Forests feature selection identified several unexpected symptoms and clinical components as relevant for septic shock, SOFA score, and survival. These discoveries can help doctors and physicians in understanding and predicting septic shock. We made the analyzed dataset and our developed software code publicly available online.

Chronic kidney disease (CKD) describes a long-term decline in kidney function and has many causes. It affects hundreds of millions of people worldwide every year. It can have a strong negative impact on patients, especially when combined with cardiovascular disease (CVD): patients with both conditions have lower survival chances. In this context, computational intelligence applied to electronic health records can provide insights to physicians that can help them make better decisions about prognoses or therapies. In this study we applied machine learning to medical records of patients with CKD and CVD. First, we predicted if patients develop severe CKD, both including and excluding information about the year it occurred or date of the last visit. Our methods achieved top mean Matthews correlation coefficient (MCC) of +0.499 in the former case and a mean MCC of +0.469 in the latter case. Then, we performed a feature ranking analysis to understand which clinical factors are most important: age, eGFR, and creatinine when the temporal component is absent; hypertension, smoking, and diabetes when the year is present. We then compared our results with the current scientific literature, and discussed the different results obtained when the time feature is excluded or included. Our results show that our computational intelligence approach can provide insights about diagnosis and relative important of different clinical variables that otherwise would be impossible to observe.

Neuroblastoma (NB) is the most common extracranial malignant tumor in children. Although the survival rate of NB has improved over the years, the outcome of NB still remains poor for over 30% of cases. A more accurate risk stratification remains a key point in the study of NB and the availability of novel prognostic biomarkers of “high-risk” at diagnosis could help improving patient stratification and predicting outcome.In this paper we show a biomarker discovery approach applied to the plasma of 172 NB patients. Plasma samples from a first cohort of NB patients and age-matched healthy controls were used for untargeted metabolomics analysis based on high-resolution mass spectrometry (HRMS). Differential expression analysis highlighted a number of metabolites annotated with a high degree of identification. Among them, 3-O-methyldopa (3-O-MD) was validated in a second cohort of NB patients using a targeted metabolite profiling approach and its prognostic potential was also analyzed by survival analysis on patients with 3 years follow-up. High expression of 3-O-MD was associated with worse prognosis in the subset of patients with stage M tumor (log-rank p < 0.05) and, among them, it was confirmed as a prognostic factor able to stratify high-risk patients older than 18 months. 3-O-MD might be thus considered as a novel prognostic biomarker of NB eligible to be included at diagnosis among catecholamine metabolite panels in prospective clinical studies. Further studies are warranted to exploit other potential biomarkers highlighted using our approach.

Background: The air traffic management (ATM) system has historically coped with a global increase in traffic demand ultimately leading to increased operational complexity. When dealing with the impact of this increasing complexity on system safety it is crucial to automatically analyse the losses of separation (LoSs) using tools able to extract meaningful and actionable information from safety reports. Current research in this field mainly exploits natural language processing (NLP) to categorise the reports,with the limitations that the considered categories need to be manually annotated by experts and that general taxonomies are seldom exploited. Methods: To address the current gaps,authors propose to perform exploratory data analysis on safety reports combining state-of-the-art techniques like topic modelling and clustering and then to develop an algorithm able to extract the Toolkit for ATM Occurrence Investigation (TOKAI) taxonomy factors from the free-text safety reports based on syntactic analysis. TOKAI is a tool for investigation developed by EUROCONTROL and its taxonomy is intended to become a standard and harmonised approach to future investigations. Results: Leveraging on the LoS events reported in the public databases of the Comisión de Estudio y Análisis de Notificaciones de Incidentes de Tránsito Aéreo and the United Kingdom Airprox Board,authors show how their proposal is able to automatically extract meaningful and actionable information from safety reports,other than to classify their content according to the TOKAI taxonomy. The quality of the approach is also indirectly validated by checking the connection between the identified factors and the main contributor of the incidents. Conclusions: Authors' results are a promising first step toward the full automation of a general analysis of LoS reports supported by results on real-world data coming from two different sources. In the future,authors' proposal could be extended to other taxonomies or tailored to identify factors to be included in the safety taxonomies.

In many decision-making scenarios, ranging from recreational activities to healthcare and policing, the use of artificial intelligence coupled with the ability to learn from historical data is becoming ubiquitous. This widespread adoption of automated systems is accompanied by the increasing concerns regarding their ethical implications. Fundamental rights, such as the ones that require the preservation of privacy, do not discriminate based on sensible attributes (e.g., gender, ethnicity, political/sexual orientation), or require one to provide an explanation for a decision, are daily undermined by the use of increasingly complex and less understandable yet more accurate learning algorithms. For this purpose, in this work, we work toward the development of systems able to ensure trustworthiness by delivering privacy, fairness, and explainability by design. In particular, we show that it is possible to simultaneously learn from data while preserving the privacy of the individuals thanks to the use of Homomorphic Encryption, ensuring fairness by learning a fair representation from the data, and ensuring explainable decisions with local and global explanations without compromising the accuracy of the final models. We test our approach on a widespread but still controversial application, namely face recognition, using the recent FairFace dataset to prove the validity of our approach.

In this paper, we deal with the classical Statistical Learning Theory’s problem of bounding, with high probability, the true risk R(h) of a hypothesis h chosen from a set H of m hypotheses. The Union Bound (UB) allows one to state that PLR^(h),δqh≤R(h)≤UR^(h),δph≥1−δ where R^(h) is the empirical errors, if it is possible to prove that P{R(h)≥L(R^(h),δ)}≥1−δ and P{R(h)≤U(R^(h),δ)}≥1−δ, when h, qh, and ph are chosen before seeing the data such that qh,ph∈[0,1] and ∑h∈H(qh+ph)=1. If no a priori information is available qh and ph are set to 12m, namely equally distributed. This approach gives poor results since, as a matter of fact, a learning procedure targets just particular hypotheses, namely hypotheses with small empirical error, disregarding the others. In this work we set the qh and ph in a distribution-dependent way increasing the probability of being chosen to function with small true risk. We will call this proposal Distribution-Dependent Weighted UB (DDWUB) and we will retrieve the sufficient conditions on the choice of qh and ph that state that DDWUB outperforms or, in the worst case, degenerates into UB. Furthermore, theoretical and numerical results will show the applicability, the validity, and the potentiality of DDWUB.