This study introduces a machine learning (ML)-based platform aimed at predicting the effectiveness of Metacognitive Training (MCT). The platform is meant to function as an experimental prototype in the scope of a clinical research project for a decision support system to assist clinicians in tailoring treatment plans for patients with psychosis. It integrates eight ML models to evaluate MCT effectiveness under a wide range of mental health questionnaires to assess a broad spectrum of psychological symptoms. By incorporating diverse measures, the platform aims to capture a comprehensive understanding of patient profiles, enabling more precise and tailored predictions for treatment personalization. Furthermore, the transparency requirements for artificial intelligence (AI) systems, as outlined in the AI Act regulation of the European Union, are addressed through the implementation of explainable AI models, using post-hoc explanations based on SHAP analysis for each predictive model. Ethical concerns related to ensuring gender-neutral behavior in the system are tackled by conducting a disparate impact analysis, which evaluates biases present in the models enhancing the system's accountability and alignment with ethical and regulatory standards.

Personalized medicine is a data-driven approach that aims to adapt patients’ diagnostics and therapies to their characteristics and needs. The availability of patients’ data is therefore paramount for the personalization of treatments on the basis of predictive models, and even more so in machine learning-based analyses. Data harmonization is an essential part of the process of data curation. This study presents research on data harmonization for the development of a harmonized retrospective database of patients in Metacognitive Training (MCT) treatment for psychotic disorders. This work is part of the European ERAPERMED 2022-292 research project entitled ‘Towards a Personalized Medicine Approach to Psychological Treatment of Psychosis’ (PERMEPSY), which focuses on the development of a personalized medicine platform for the treatment of psychosis. The study integrates information from 22 studies into a common format to enable a data analytical approach for personalized treatment. The harmonized database comprises information about 698 patients who underwent MCT and includes a wide range of sociodemographic variables and psychological indicators used to assess a patient’s mental health state. The characteristics of patients participating in the study are analyzed using descriptive statistics and exploratory data analysis.

Modern power grids are increasingly challenged by the growing reliance on renewable energy sources. Due to their inherent intermittency, these sources can cause voltage fluctuations and phase imbalances, particularly during grid disturbances. Among these perturbations, voltage sags are the most critical ones, occurring within seconds or even milliseconds. A required mitigation strategy involves injecting reactive current into the phase experiencing low voltage. Traditional approaches for determining the appropriate amount of reactive current rely on grid codes, which define the minimum value based on measured voltages; therefore, there is no optimization, nor adaptability to better attend to the grid’s needs. In this study, we propose an alternative solution based on Soft Actor-Critic (SAC), a model-free and off-policy reinforcement learning (RL) algorithm which addresses the weaknesses of previous approaches. Simulation results during the inference phase demonstrate that the SAC-based method closely matches the performance of optimization-based approaches, while offering better generalization to unseen data within a fast response time in milliseconds.

Gas turbines play a key role in generating power. It is really important that they work efficiently, safely, and reliably. However, their performance can be adversely affected by factors such as component wear, vibrations, and temperature fluctuations, often leading to abnormal patterns indicative of potential failures. As a result, anomaly detection has become an area of active research. Matrix Profile (MP) methods have emerged as a promising solution for identifying significant deviations in time series data from normal operational patterns. While most existing MP methods focus on vibration analysis of gas turbines, this paper introduces a novel approach using the outlet power signal. This modified approach, termed Cluster-based Matrix Profile (CMP) analysis, facilitates the identification of abnormal patterns and subsequent anomaly detection within the gas turbine engine system. Significantly, CMP analysis not only accelerates processing speed, but also provides user-friendly support information for operators. The experimental results on real-world gas turbines demonstrate the effectiveness of our approach in the early detection of anomalies and potential system failures.

During the last two decades, the operation of the electrical grid has undergone significant changes. This evolution is closely tied to the integration of power electronics into distributed generation systems, which led to increased utilization of renewable energy and as a consequence mitigating climate change by lowering emissions. On the other hand, artificial intelligence plays a considerable role in shaping the development and progress of various technologies, such as the electrical grid. This work presents a study for the application of Reinforcement Learning (RL) tools in distributed generation systems. The objective of using RL is to address voltage perturbations in real time. RL will be useful to maximize the resilience of the system in the event of short circuits of a short duration and minimize the risk of disconnect.

Building on a previously developed partially synthetic data generation algorithm utilizing data visualization techniques, this study extends the novel algorithm to generate fully synthetic tabular healthcare data. In this enhanced form, the algorithm serves as an alternative to conventional methods based on Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs). By iteratively applying the original methodology, the adapted algorithm employs UMAP (Uniform Manifold Approximation and Projection), a dimensionality reduction technique, to validate generated samples through low-dimensional clustering. This approach has been successfully applied to three healthcare domains: prostate cancer, breast cancer, and cardiovascular disease. The generated synthetic data have been rigorously evaluated for fidelity and utility. Results show that the UMAP-based algorithm outperforms GAN- and VAE-based generation methods across different scenarios. In fidelity assessments, it achieved smaller maximum distances between the cumulative distribution functions of real and synthetic data for different attributes. In utility evaluations, the UMAP-based synthetic datasets enhanced machine learning model performance, particularly in classification tasks. In conclusion, this method represents a robust solution for generating secure, high-quality synthetic healthcare data, effectively addressing data scarcity challenges.

Social robots interacting with people in public spaces may access and collect their personal information, which raises privacy concerns regarding the disclosure of personal information. This paper aims to investigate factors impacting individuals’ intention to disclose personal information to a social robot in public spaces and evaluate the actual disclosure during the interaction with the robot. For this purpose, a model is proposed to predict people’s intentions to disclose information to a social robot. We conducted our experiment at a public festival with more than 100 participants using the social robot ARI. The findings reveal the substantial impact of factors including risk beliefs, trusting beliefs, perceived enjoyment, and social influence on the intention to disclose personal information. Moreover, they reveal that although only a small percentage (6.20%) of people had the intention to disclose information to the social robot, most participants (98.00%) finally disclosed their personal information.

In healthcare, vast amounts of data are increasingly collected through sensors for smart health applications and patient monitoring or diagnosis. However, such medical data often comprise sensitive patient information, posing challenges regarding data privacy, and are resource-intensive to acquire for significant research purposes. In addition, the common case of lack of information due to technical issues, transcript errors, or differences between descriptors considered in different health centers leads to the need for data imputation and partial data generation techniques. This study introduces a novel methodology for partially synthetic tabular data generation, designed to reduce the reliance on sensor measurements and ensure secure data exchange. Using the UMAP (Uniform Manifold Approximation and Projection) visualization algorithm to transform the original, high-dimensional reference data set into a reduced-dimensional space, we generate and validate synthetic values for incomplete data sets. This approach mitigates the need for extensive sensor readings while addressing data privacy concerns by generating realistic synthetic samples. The proposed method is validated on prostate and breast cancer data sets, showing its effectiveness in completing and augmenting incomplete data sets using fully available references. Furthermore, our results demonstrate superior performance in comparison to state-of-the-art imputation techniques. This work makes a dual contribution by not only proposing an innovative method for synthetic data generation, but also studying and establishing a formal framework to understand and solve synthetic data generation and imputation problems in sensor-driven environments.

Internet of Things (IoT) systems are becoming increasingly complex due to heterogeneity of devices and requirements for real-time processing and decision making. In this context, Artificial Intelligence (AI) technologies provide powerful capabilities for endowing IoT devices with intelligent services, leading to the so-called Artificial Intelligence of Things (AIoT). The operator is in the middle of this complexity, trying to understand the situation and make effective real-time decisions. Hence, human factors, especially cognitive ones, are a major issue to be addressed. The human cognitive part must be framed together with intelligent artefacts, requiring a systematic approach in the domain of joint cognitive systems. New software development methods in the form of assistants and wizards are necessary to help operators to be context-aware and reduce their technical workload regarding coding or computer-oriented skills, focusing on the task or service at hand. Building on previous research on the role of the human worker in an AIoT environment, this article analyses the described situation in terms of human cyber–physical systems, with the aim of proposing a conceptual framework for these assistance systems at the cognitive level. Two illustrative examples are described to validate the effectiveness of the proposed framework in collaborative tasks.

Existing research has shown the effectiveness of genetic strategies in generating Petrin-Net (PN)-based controllers, but limitations exist in the ease of controller generation due to the designer’s ability and the system’s complexity. In the case of automated controller generators based on genetic programming (GP), limitations arise from the static nature of their chromosome over the evolution process. In this short paper we introduce a first discrete PN-based controller designer that can accept systems modeled either continuously or discretely, making it more flexible in handling a wide range of systems. By utilizing genetic algorithms and PNs, the program can generate controllers tailored to the specific requirements of a given system, including the optimal size of the controller. This novel approach has the potential for far-reaching applications in various fields.

Machine learning algorithms and the increasing availability of data have radically changed the way how decisions are made in today’s Industry. A wide range of algorithms are being used to monitor industrial processes and predict process variables that are difficult to be measured. Maintenance operations are mandatory to tackle in all industrial equipment. It is well known that a huge amount of money is invested in operational and maintenance actions in industrial gas turbines (IGTs). In this paper, two variations of autoencoders were used to analyse the performance of an IGT after major maintenance. The data used to analyse IGT conditions were ambient factors, and measurements were performed using several sensors located along the compressor. The condition assessment of the industrial gas turbine compressor revealed significant changes in its operation point after major maintenance; thus, this indicates the need to update the internal operating models to suit the new operational mode as well as the effectiveness of autoencoder-based models in feature extraction. Even though the processing performance was not compromised, the results showed how this autoencoder approach can help to define an indicator of the compressor behaviour in long-term performance.

We propose a hierarchical framework for collaborative intelligent systems. This framework organizes research challenges based on the nature of the collaborative activity and the information that must be shared, with each level building on capabilities provided by lower levels. We review research paradigms at each level, with a description of classical engineering-based approaches and modern alternatives based on machine learning, illustrated with a running example using a hypothetical personal service robot. We discuss cross-cutting issues that occur at all levels, focusing on the problem of communicating and sharing comprehension, the role of explanation and the social nature of collaboration. We conclude with a summary of research challenges and a discussion of the potential for economic and societal impact provided by technologies that enhance human abilities and empower people and society through collaboration with intelligent systems.