Management and Sustainable Exploitation of Marine Environments through Smart Monitoring and Automation

Innovative materials that behave like robots to combat plastic pollution

The growing plastic pollution crisis demands novel approaches, with innovative materials that mimic robotic behaviors emerging as a promising solution. This approach explores the development and application of smart materials that can autonomously engage in plastic waste removal, functioning like robots under various environmental conditions. We focus on materials activated by light, magnetic fields, chemical fuels, and ion exchange, which are designed to target and remove plastic waste efficiently. The key properties of these materials, such as self-activation, adaptability, and precision that enable them to function autonomously in waste management systems, are examined. The integration of these innovative materials offers significant advantages, including faster waste processing, reduced human exposure to hazardous waste, and enhanced sorting accuracy. Additionally, this review evaluates the environmental impact, scalability, and cost-effectiveness of these materials in comparison to traditional methods. Finally, the potential of these materials to play a central role in sustainable plastic waste management and contribute to a circular economy is discussed.

An update for various applications of Artificial Intelligence (AI) for detection and identification of marine environmental pollutions: A bibliometric analysis and systematic review

Marine environmental pollution is one of the growing concerns of humans all over the world. Therefore, managing these marine pollutants has been a crucial matter for scientists in recent decades. Thus, researchers have tried to implement artificial intelligence (AI) to handle marine environmental pollutants. Therefore, in this manuscript, we performed a bibliometric analysis to understand the main applications of AI for managing marine environments. Therefore, we examined both PubMed online database and Google Scholar to find any research articles that discuss the applications of AI in managing marine environmental pollution. Ultimately, we found that AI can detect, locate, and even predict aquatic contaminants like oil fingerprinting, oil spills, oil spill damage, oil slicks, forecasting marine water quality, water quality development, harmful algal blooms, benthic sediment toxicity, as well as detection of marine debris with high accuracy.

Unveiling wearables: exploring the global landscape of biometric applications and vital signs and behavioral impact

The development of neuroscientific techniques enabling the recording of brain and peripheral nervous system activity has fueled research in cognitive science. Recent technological advancements offer new possibilities for inducing behavioral change, particularly through cost-effective Internet-based interventions. However, limitations in laboratory equipment volume have hindered the generalization of results to real-life contexts. The advent of Internet of Things (IoT) devices, such as wearables, equipped with sensors and microchips, has ushered in a new era in behavior change techniques. Wearables, including smartwatches, electronic tattoos, and more, are poised for massive adoption, with an expected annual growth rate of 55% over the next five years. These devices enable personalized instructions, leading to increased productivity and efficiency, particularly in industrial production. Additionally, the healthcare sector has seen a significant demand for wearables, with over 80% of global consumers willing to use them for health monitoring. This research explores the primary biometric applications of wearables and their impact on users' well-being, focusing on the integration of behavior change techniques facilitated by IoT devices. Wearables have revolutionized health monitoring by providing real-time feedback, personalized interventions, and gamification. They encourage positive behavior changes by delivering immediate feedback, tailored recommendations, and gamified experiences, leading to sustained improvements in health. Furthermore, wearables seamlessly integrate with digital platforms, enhancing their impact through social support and connectivity. However, privacy and data security concerns must be addressed to maintain users' trust. As technology continues to advance, the refinement of IoT devices' design and functionality is crucial for promoting behavior change and improving health outcomes. This study aims to investigate the effects of behavior change techniques facilitated by wearables on individuals' health outcomes and the role of wearables in promoting a healthier lifestyle.

Design and Verification of Deep Submergence Rescue Vehicle Motion Control System

A six degree-of-freedom (DOF) motion control system for docking with a deep submergence rescue vehicle (DSRV) test platform was the focus of this study. The existing control methods can meet the general requirements of underwater operations, but the complex structures or multiple parameters of some methods have prevented them from widespread use. The majority of the existing methods assume the heeling effect to be negligible and ignore it, achieving motion control in only four or five DOFs. In view of the demanding requirements regarding positions and inclinations in six DOFs during the docking process, the software and hardware architectures of the DSRV platform were constructed, and then sparse filtering technology was introduced for data smoothing. Based on the adaptive control strategy and with a consideration of residual static loads, an improved S-plane control method was developed. By converting the force (moment) calculated by the controller to the body coordinate system, the complexity of thrust allocation was effectively reduced, and the challenge of thrust allocation in the case of a high inclination during dynamic positioning was solved accordingly. The automatic control of the trimming angle and heeling angle was realized with the linkage system of the ballast tank and pump valve. A PID method based on an intelligent integral was proposed, which not only dealt with the integral "saturation" problem, but also reduced the steady-state error and overshooting. Water pool experiments and sea trials were carried out in the presence of water currents for six-DOF motion control. The responsiveness and precision of the control system were verified by the pool experiment and sea trial results and could meet the control requirements in engineering practice. The reliability and operational stability of the proposed control system were also verified in a long-distance cruise.

Research on an Extensible Monitoring System of a Seafloor Observatory Network in Laizhou Bay

An extensible remote monitoring system for a seafloor observatory network in Laizhou Bay was established to realize long-term, continuous and on-line monitoring for a marine ranching environment. This paper deals with data communication, device management and data quality control. A control model is introduced that is structured into four layers, enabling bidirectional information flow. Based on the control model, the standardized communication protocol and device object model-oriented dynamic management method are designed as plug-and-play, for data processing and control of a large number of devices . An improved data quality control method is proposed to reduce the data error rate. The monitoring system was developed based on socket network programming, MySQL database technologies and modular ideas. The seafloor observatory network was successfully deployed in Laizhou Bay marine ranching. The experimental results demonstrate that the monitoring system obtains better performance. The proposed algorithms can also be used in many other similar systems with adaptive requirements.

Improved RRT Algorithm for AUV Target Search in Unknown 3D Environment

Due to the complexity of the marine environment, underwater target search and interception is one of the biggest problems faced by an autonomous underwater vehicle (AUV). At present, there is quite a lot of research in terms of the two-dimensional environment. This paper proposes an improved rapidly exploring random trees (RRT) algorithm to solve the problem of target search and interception in an unknown three-dimensional (3D) environment. The RRT algorithm is combined with rolling planning and node screening to realize path planning in an unknown environment, and then the improved RRT algorithm is applied to the search and interception process in a 3D environment. Combined with the search decision function and the three-point numerical differential prediction method, the RRT algorithm can search for and effectively intercept the target. Numerical simulations in various situations show the superior performance, in terms of time and accuracy, of the proposed approach.