Asset Management (AM) is quickly transforming due to the digital revolution induced by Industry 4.0, in which Cyber–Physical Systems (CPS) and Digital Twins (DT) are taking key positions in monitoring and optimizing physical assets. With more intelligent functionalities arising in industrial contexts, Machine Learning (ML) has transitioned from playing a supporting role to becoming a core constituent of asset operation. However, while the Asset Administration Shell (AAS) has become an industry standard format for digital asset representation, incorporating ML models into this format is a significant challenge. In this research, a control valve, a common asset in industrial equipment, is used to explore the modeling of a machine learning model as an AAS submodel, including its related elements, such as parameters, hyperparameters, and metadata, in accordance with the latest guidelines issued by the Industrial Digital Twin Association (IDTA) in early 2025. The main contribution of this work is to clarify basic machine learning principles while demonstrating their alignment with the AAS framework, hence facilitating the further development of smart and interoperable DTs in modern industrial environments.

Introduction: Despite the end of the SARS-CoV-2 pandemic, the medical field continues to address several lasting effects, the most notable being long COVID. However, COVID-19 presents another specific challenge that complicates diagnosis: the similarity of its symptoms with those of other viral diseases, particularly among various SARS strains. This overlap makes it difficult to identify distinct and meaningful symptom patterns as they develop. This study proposes a dimensionality reduction approach combined with a clustering technique to visually analyse structural similarities among SARS-infected individuals, aiming to determine whether aspects such as case progression and diagnosis impact these patterns.Methods: This analysis utilised the t-Distributed Stochastic Neighbour Embedding (t-SNE) algorithm for dimensionality reduction, combined with Gower's distance to handle categorical data, and k-means clustering. The study focused on symptoms, case progression, and diagnoses of SARS-CoV-2 and unspecified SARS cases using data from the Brazilian SARS dataset for São Paulo State during 2020 and 2021. The process began with a visual analysis aimed at identifying structural patterns in the symptom data, highlighting potential similarities between COVID-19 patients and those diagnosed with unspecified SARS. Following this, an intra-cluster analysis was performed to investigate the common features that defined each cluster, providing insights into shared characteristics among grouped individuals.Results: The analysis revealed that both diagnoses share substantial similarities, particularly in the presence or absence of COVID-19-related symptoms, even when the majority of individuals were diagnosed with unspecified SARS.Discussion: The analysis is crucial, as Brazil was one of the countries most severely affected by the pandemic, experiencing profound impacts across multiple dimensions.

Efficiently recognising severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) symptoms enables a quick and accurate diagnosis to be made, and helps in mitigating the spread of the coronavirus disease 2019. However, the emergence of new variants has caused constant changes in the symptoms associate with COVID-19. These constant changes directly impact the performance of machine-learning-based diagnose. In this context, considering the impact of these changes in symptoms over time is necessary for accurate diagnoses. Thus, in this study, we propose a machine-learning-based approach for diagnosing COVID-19 that considers the importance of time in model predictions. Our approach analyses the performance of XGBoost using two different time-based strategies for model training: month-to-month and accumulated strategies. The model was evaluated using known metrics: accuracy, precision, and recall. Furthermore, to explain the impact of feature changes on model prediction, feature importance was measured using the SHAP technique, an XAI technique. We obtained very interesting results: considering time when creating a COVID-19 diagnostic prediction model is advantageous.

The efficient recognition of symptoms in viral infections holds promise for swift and precise diagnosis, thus mitigating health implications and the potential recurrence of infections. COVID-19 presents unique challenges due to various factors influencing diagnosis, especially regarding disease symptoms that closely resemble those of other viral diseases, including other strains of SARS, thus impacting the identification of useful and meaningful symptom patterns as they emerge in infections. Therefore, this study proposes an association rule mining approach, utilising the Apriori algorithm to analyse the similarities between individuals with confirmed SARS-CoV-2 diagnosis and those with unspecified SARS diagnosis. The objective is to investigate, through symptom rules, the presence of COVID-19 patterns among individuals initially not diagnosed with the disease. Experiments were conducted using cases from Brazilian SARS datasets for São Paulo State. Initially, reporting percentage similarities of symptoms in both groups were analysed. Subsequently, the top ten rules from each group were compared. Finally, a search for the top five most frequently occurring positive rules among the unspecified ones, and vice versa, was conducted to identify identical rules, with a particular focus on the presence of positive rules among the rules of individuals initially diagnosed with unspecified SARS.