Aging ships and offshore structures face harsh environmental and operational conditions in remote areas, leading to age-related damages such as corrosion wastage, fatigue cracking, and mechanical denting. These deteriorations, if left unattended, can escalate into catastrophic failures, causing casualties, property damage, and marine pollution. Hence, ensuring the safety and integrity of aging ships and offshore structures is paramount and achievable through innovative healthcare schemes. One such paradigm, digital healthcare engineering (DHE), initially introduced by the final coauthor, aims at providing lifetime healthcare for engineered structures, infrastructure, and individuals (e.g., seafarers) by harnessing advancements in digitalization and communication technologies. The DHE framework comprises five interconnected modules: on-site health parameter monitoring, data transmission to analytics centers, data analytics, simulation and visualization via digital twins, artificial intelligence-driven diagnosis and remedial planning using machine and deep learning, and predictive health condition analysis for future maintenance. This article surveys recent technological advancements pertinent to each DHE module, with a focus on its application to aging ships and offshore structures. The primary objectives include identifying cost-effective and accurate techniques to establish a DHE system for lifetime healthcare of aging ships and offshore structures—a project currently in progress by the authors.

Decentralized E/E architectures (EEAs) are facing challenges and bottlenecks in implementing new features and technologies. The shift towards centralized EEAs has many challenges and needs to be handled pragmatically by considering concurrency with the existing EEAs. To address the challenges of architectural shift, the paper showcases the quantitative comparison of EEAs and visualizes the flow of shifting sub-function and hardware blocks using the Sankey diagram. The observations from the diagram as a result will support OEMs to analyse and take decisions on the shift while developing EEAs.

Despite their increasing popularity, n-of-1 designs employ data analyses that might not be as complete and powerful as they could be. Borrowing from existing advances in educational and psychological research, this article presents a few techniques and references for rigorous data analytic techniques in n-of-1 research.

This study aimed to identify and understand the major topics of discussion under the #sustainability hashtag on Twitter (now known as “X”) and understand user engagement. The sharp increase in social media usage combined with a rise in climate anomalies in recent years makes the area of sustainability with respect to social media a critical topic. Python was used to gather Twitter posts between January 1, 2023, and March 1, 2023. User engagement metrics were analyzed using a variety of statistical analysis methods, including keyword-frequency analysis and Latent Dirichlet Allocation (LDA), which were used to identify significant topics of discussion under the #sustainability hashtag. Additionally, histograms and scatter plots were used to visualize user engagement. LDA analysis was conducted with 7 topics after trials were run with various topics and results were analyzed to determine which number of topics best fit the dataset. The frequency analysis provided a basic overview of the discourse surrounding #sustainability with the topics of technology, business and industry, environmental awareness, and discussion of the future. The LDA model provided a more comprehensive view, including additional topics such as Environmental, Social, and Governance (ESG) and infrastructure, investing, collaboration, and education. These findings have implications for researchers, businesses, organizations, and politicians seeking to align their strategies and actions with the major topics surrounding sustainability on Twitter to have a greater impact on their audience. Researchers can use the results of this study to guide further research on the topic or contextualize their study with existing literature within the field of sustainability.

PEPAdb (Prehistoric Europe's Personal Adornment Database) is a long-term, open-ended project that aims to improve access to archaeological data online. Its website (https://pepadb.us.es) publishes and analyses datasets about prehistoric personal adornment, drawing on the results of various research projects and bibliographic references.

Team composition in Project Based Learning is the first task for the class and has a great impact on the learning experience. Anyway, little space is dedicated in literature about team composition, considering their personal inclinations towards design tasks.

For these reasons we propose a tool that aims to map the design skills of students to optimise team composition. The tool is based on a questionnaire grounded in the design theory and aims at measuring the willingness of students at performing certain design tasks. The results of the questionnaires are analysed using Principal Component Analysis to normalise each students’ answers to the whole class, and to show the distribution of students in the space of engineering design skills.

We present the design process of the tool, and a first experimentation on two classes of master's degree students in Management Engineering and Data Science, testing the tool on a total of 72 students. The results are promising and demonstrate the robusteness of the questionnaire and of the analytical method. Also, we propose next steps for our research activity, calling for other researchers to test our method in different contexts.

The categorisation of minerals and their related names, such as synonyms, obsolete or historical names, varieties or mixtures, is an asset for designing an interoperable and consistent mineralogical data warehouse. An enormous amount of this data, provided by mindat.org and other resources, was reviewed and analysed during the research. The analysis indicates the existence of several categories of (1) the abstract titles or designations representing the link to the original material or a group of names or substances without actual physical representation, and (2) the unique names representing actual physical material, compounds, or an aggregate of one or more minerals. A revision of the dependency between the categories attributes stored in a database (e.g. chemical properties, physical properties) and their classification status assigned allowed us to design a robust prototype for maintaining database integrity and consistency. The proposed scheme allows standardisation and structuring of officially regulated and maintained species, e.g. IMA-approved, and, in addition, unregulated ones.

We introduce an approach for damage detection in gearboxes based on the analysis of sensor data with the multi-resolution dynamic mode decomposition (mrDMD). The application focus is the condition monitoring of wind turbine gearboxes under varying load conditions, in particular irregular and stochastic wind fluctuations. We analyze data stemming from a simulated vibration response of a simple nonlinear gearbox model in a healthy and damaged scenario and under different wind conditions. With mrDMD applied on time-delay snapshots of the sensor data, we can extract components in these vibration signals that highlight features related to damage and enable its identification. A comparison with Fourier analysis, time synchronous averaging, and empirical mode decomposition shows the advantages of the proposed mrDMD-based data analysis approach for damage detection.