Offshore wind turbines (OWTs) empoly various foundation types, among which Jacket-type offshore wind turbines (JOWTs) are often used in shallow waters with challenging soil conditions due to their lattice framework foundations and multiple anchoring points. However, prolonged exposure to harsh marine environments (e.g. storms) and age-related degradation issues like corrosion, fatigue cracking, and mechanical damage increases failure risks. To address these issues, this paper introduces a Digital Healthcare Engineering (DHE) framework, which provides a proactive strategy for enhancing the safety and sustainability of JOWTs: (1) Real-time health monitoring using IoT; (2) Data transmission via advanced communication technologies; (3) Analytics and simulations using digital twins; (4) AI-powered diagnostics and recommendations; as well as (5) Predictive analysis for maintenance planning. The paper reviews recent technological advances that support each DHE module, assesses the framework's feasibility. Additionally, a prototype DHE system is proposed to enable continuous, early fault detection, and health assessment.

Precision agriculture (PA), also known as smart farming, has emerged as an innovative solution to address contemporary challenges in agricultural sustainability. A particular sector within PA, precision viticulture (PV), is specifically tailored for vineyards. The advent of the Internet of Things (IoT) has facilitated the acquisition of higher resolution meteorological and soil data obtained through in situ sensing. The integration of machine learning (ML) with IoT-enabled farm machinery stands at the forefront of the forthcoming agricultural revolution. These data allow ML-based forecasting as an alternative to conventional approaches, providing agronomists with predictive tools essential for improved land productivity and crop quality. This study conducts a thorough examination of vineyards with a specific focus on three key aspects of PV: mitigating frost damage, analyzing soil moisture levels, and addressing grapevine diseases. In this context, several ML-based models are proposed in a real-world scenario involving a vineyard located in Southern Italy. The test results affirm the feasibility and efficacy of the ML models, demonstrating their potential to revolutionize vineyard management and contribute to sustainable agricultural practices.