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.

Landslides are mass movements of rock, soil, or debris under the influence of gravity. These phenomena occur due to the loss of slope stability or imbalance of external loads. The intensity and consequences of landslides depend on various factors such as topography, geological structure, and precipitation regime. This study investigates the characteristics of rainfall-induced landslides in red basaltic soils on the basis of field investigations, geotechnical surveys, and slope stability modeling under anthropogenic triggers. The results indicate a close relationship between soil moisture and shear strength parameters, which significantly influence slope stability. A real-time observation system recorded groundwater level fluctuation in relation to surface runoff and precipitation rates. It is revealed that intense rainfall and low temperatures regulate soil moisture, resulting in a reduction of cohesion and shear strength parameters. These findings enhance the understanding of landslide mechanism in basaltic soil regions, which are highly sensitive to precipitation. The results also highlight that human activities play a significant role in triggering landslides. Therefore, a real-time monitoring system for rainfall, soil moisture, and groundwater is essential for early warning and supports the integration of smart technologies and Internet of Things (IoT) solutions in natural disaster management.

The stability of rock and soil masses has become increasingly critical due to large-scale expansion and landfilling, resulting in frequent landslides that pose significant threats to safety and property. Consequently, soil slope stability monitoring is essential. To mitigate slope instability risks, this study investigates soil slope stability monitoring using big data technology within the context of the internet of things. The research examines slope monitoring techniques and summarizes various methods for detecting slope deformation. By monitoring displacement and deformation, the operational status of slopes can be assessed, safety evaluated, disasters prevented, and adverse social impacts avoided. Collected geological data support the development of slope models, enabling analysis under different damage conditions. The findings indicate that 50% damage corresponds to a warning threshold, while 80% damage triggers an alarm. Simulation results show that slope stability increases with higher internal friction angle and cohesion but decreases as the slope angle increases.

Agriculture is one of the prime economical sources of India and most of the people directly or indirectly depend on farming. The researchers are focusing on plant ailment detection and managing the imbalanced nutrition in plants. Automation is introduced in agricultural fields and most of these automation strategies use the Internet of Things (IoT) for enhance productivity and automate processes. With the help of several deep and machine learning approaches the endless decision-making performance is performed. Here, the endless decision performance shows appropriate outcomes which helps to solve the unstructured problems in smart farming. It is monitored that the traditional analysis doesn't have enough decision-making ability in the selection of fertilizer quantity that is to be used in farming. This inability leads to crop ailments and that affects the lifestyle of humans too. So, the prior detection of ailments in crops is essential. Enforcing Smart Agriculture is a hot topic in research nowadays to overcome crop damage in the future. Therefore, a new IoT-based smart farming model using deep learning is proposed to increase crop yield. By detecting disease, pests, smart irrigation, and yield, the smart farming model can reduce the amount of water and chemicals used in agriculture. This smart farming model consists of four phases a) disease prediction, b) pest detection c) smart irrigation, and d) yield prediction. In the first phase, the crop images are gathered from online datasets. The diseases in crops are predicted using Multiscale Adaptive CNN with LSTM layer (MA-CNN-LSTM), where the parameters in MA-CNN-LSTM are optimized using Advanced Mountaineering Team-Based Optimization Algorithm (AMTBO). In the second phase, the input images are given to MA-CNN-LSTM to detect crop pests. Here, the AMTBO is utilized for tuning parameters. In the third phase, the soil quality and environment data are fed into the Multi-scale Adaptive 1DCNN with LSTM layer (MA-1D CNN-LSTM) to predict the smart irrigation, where the parameter optimization is done using the AMTBO. Smart irrigation enhances the growth of crops and minimizes water usage. In the final phase, the input data such as crop quality, soil quality, and environment data are given to the MA-1D CNN-LSTM to check the overall yield prediction in an agricultural region. Here, the parameters in MA-1D CNN-LSTM are optimized via the AMTBO. The simulation results are compared with other algorithms and classification techniques to check the performance of the developed model.

The development of real-time early warning systems is crucial for mitigating landslide risks. Although internet coverage is extensive in urban areas, it often fails to reach remote locations such as mountainous regions. The low-power wide area (LPWA) communication network offers a viable alternative for transmitting data from landslide early warning system (LEWS) sensors to a central server. To develop an accurate and reliable LEWS, it is essential to establish appropriate thresholds for warning triggers. This study conducted a series of laboratory experiments on slope models, both with and without vertical cracks. The models were subjected to varying rainfall intensities to investigate the mechanisms of slope failure. The objective of this paper was to evaluate a cost-effective and sustainable LEWS based on internet of things using the Internet (WiFi) and LPWA for data transmission, and to monitor slope vulnerability. During the experiments, volumetric water content, pore water pressure, and tilt angle were measured. Thresholds for critical volumetric water content, pore water pressure rate, and tilt rate were proposed to define warning stages. The results contribute to enhancing the advancement of early warning systems, which are crucial for mitigating the risks associated with landslides.

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.