A COMPREHENSIVE UNDERWATER DOCKING APPROACH THROUGH EFFICIENT DETECTION AND STATION KEEPING WITH LEARNING-BASED TECHNIQUES

The growing movement toward sustainable use of ocean resources is driven by the pressing need to alleviate environmental and human stressors on the planet and its oceans. From monitoring the food web to supporting sustainable fisheries and observing environmental shifts to protect against the effects of climate change, ocean observations significantly impact the Blue Economy. Acknowledging the critical role of Autonomous Underwater Vehicles (AUVs) in achieving persistent ocean exploration, this research addresses challenges focusing on the limited energy and storage capacity of AUVs, introducing a comprehensive underwater docking solution with a specific emphasis on enhancing the terminal homing phase through innovative vision algorithms leveraging neural networks.The primary goal of this work is to establish a docking procedure that is failure-tolerant, scalable, and systematically validated across diverse environmental conditions. To fulfill this objective, a robust dock detection mechanism has been developed that ensures the resilience of the docking procedure through \comment{an} improved detection in different challenging environmental conditions. Additionally, the study addresses the prevalent issue of data sparsity in the marine domain by artificially generating data using CycleGAN and Artistic Style Transfer. These approaches effectively provide sufficient data for the docking detection algorithm, improving the localization of the docking station.Furthermore, this work introduces methods to compress the learned docking detection model without compromising performance, enhancing the efficiency of the overall system. Alongside these advancements, a station-keeping algorithm is presented, enabling the mobile docking station to maintain position and heading while awaiting the arrival of the AUV. To leverage the sensors onboard and to take advantage of the computational resources to their fullest extent, this research has demonstrated the feasibility of simultaneously learning docking detection and marine wildlife classification through multi-task and transfer learning. This multifaceted approach not only tackles the limitations of AUVs' energy and storage capacity but also contributes to the robustness, scalability, and systematic validation of underwater docking procedures, aligning with the broader goals of sustainable ocean exploration and the blue economy.</p

ROSEBUD: A Deep Fluvial Segmentation Dataset for Monocular Vision-Based River Navigation and Obstacle Avoidance

Obstacle detection for autonomous navigation through semantic image segmentation using neural networks has grown in popularity for use in unmanned ground and surface vehicles because of its ability to rapidly create a highly accurate pixel-wise classification of complex scenes. Due to the lack of available training data, semantic networks are rarely applied to navigation in complex water scenes such as rivers, creeks, canals, and harbors. This work seeks to address the issue by making a one-of-its-kind River Obstacle Segmentation En-Route By USV Dataset (ROSEBUD) publicly available for use in robotic SLAM applications that map water and non-water entities in fluvial images from the water level. ROSEBUD provides a challenging baseline for surface navigation in complex environments using complex fluvial scenes. The dataset contains 549 images encompassing various water qualities, seasons, and obstacle types that were taken on narrow inland rivers and then hand annotated for use in semantic network training. The difference between the ROSEBUD dataset and existing marine datasets was verified. Two state-of-the-art networks were trained on existing water segmentation datasets and tested for generalization to the ROSEBUD dataset. Results from further training show that modern semantic networks custom made for water recognition, and trained on marine images, can properly segment large areas, but they struggle to properly segment small obstacles in fluvial scenes without further training on the ROSEBUD dataset

Dynamic Obstacle Avoidance for USVs Using Cross-Domain Deep Reinforcement Learning and Neural Network Model Predictive Controller

This work presents a framework that allows Unmanned Surface Vehicles (USVs) to avoid dynamic obstacles through initial training on an Unmanned Ground Vehicle (UGV) and cross-domain retraining on a USV. This is achieved by integrating a Deep Reinforcement Learning (DRL) agent that generates high-level control commands and leveraging a neural network based model predictive controller (NN-MPC) to reach target waypoints and reject disturbances. A Deep Q Network (DQN) utilized in this framework is trained in a ground environment using a Turtlebot robot and retrained in a water environment using the BREAM USV in the Gazebo simulator to avoid dynamic obstacles. The network is then validated in both simulation and real-world tests. The cross-domain learning largely decreases the training time (28%) and increases the obstacle avoidance performance (70 more reward points) compared to pure water domain training. This methodology shows that it is possible to leverage the data-rich and accessible ground environments to train DRL agent in data-poor and difficult-to-access marine environments. This will allow rapid and iterative agent development without further training due to the change in environment or vehicle dynamics

Heavy Metals in Oysters, Shrimps and Crabs from Lagoon Systems in the Southern Gulf of México

Abstract Lagoon systems in the southern Gulf of Mexico are highly productive. These aquatic systems have been severely negatively impacted by anthropogenic and industrial activities. The objective of this research was to estimate the concentration of heavy metals Pb, Cu, Cd and Zn in oysters, shrimp and crabs from the Carmen-Pajonal-Machona and Mecoacán lagoon systems in Tabasco, México. Samples were collected from fishing zones within these lagoon systems and included oysters Crassostrea virginica, and crustaceans such as Litopenaeus setiferus (shrimp) and Callinectes sapidus (crab). Concentrations of Pb, Cu, Cd and Zn were determined by atomic absorption using flame spectrophotometry. The heavy metal concentration pattern in oysters, shrimp and crab in the Carmen-Pajonal-Machona system was Cu &gt; Pb &gt; Cd. The maximum average concentration of Cu was 259.12 ± 12.312 in oyster; 0.516 ± 0.154 in shrimp, and in crab 0.907 ± 0.273 µg g -1 . Pb had a maximum concentration of 1.37 ± 0.77 in oyster, in shrimp was 0.059 ± 0.044, and for crab was 0.0055 µg g -1 (p&gt;0.05), while in the Mecoacán lagoon system the pattern showed Pb &gt; Cd &gt; Zn. The maximum average concentration of Pb was 321.15 ± 28.828 µg g -1 , the minimum was 84.70 ± 8.612 µg g -1 . The highest concentration of Cd was 63.74 ± 8.446 µg g -1 , and the minimum 13.00 ± 0.64 µg g -1 . For Zn the maximum average concentration obtained was 24.42 ± 2.665 µg g -1

Acupoint catgut embedding therapy with moxibustion reduces the risk of diabetes in obese women

Background: Obesity is a major health problem worldwide for which conventional therapy efficacy is limited. Traditional Chinese medicine, particularly body acupoint stimulation, provides an alternative, effective, and safe therapy for this medical challenge. The present study was designed to compare the effects of distinct methods to stimulate the same set of acupoints, on anthropometric and biochemical parameters in obese women. Materials and Methods: Ninety-nine obese women were randomly assigned to six groups of treatment: Acupuncture with moxibustion, long needle acupuncture with moxibustion, electroacupuncture (EA), EA with moxibustion, embedded catgut with moxibustion (CGM) and sham acupuncture as control. Obesity-related parameters, including body weight, body mass index (BMI), waist and hip circumferences, waist/hip ratio, biochemical parameters (triglycerides, cholesterol, glucose, insulin) and homeostasis model of assessment - insulin resistance (HOMA-IR) index, were determined before and after each treatment. Results: Body weight and BMI were significantly reduced in response to all treatments. Interestingly, acupoint catgut embedding therapy combined with moxibustion was the only treatment that produced a significant reduction in body weight (3.1 ± 0.2 kg, P < 0.001), BMI (1.3 ± 0.1 kg/m 2 , P < 0.001), insulin (3.5 ± 0.8 mcU/ml, P < 0.1) and HOMA-IR (1.4 ± 0.2 units, P < 0.01) in comparison with sham group. Furthermore, this treatment was able to bring back obese women to a state of insulin sensitivity, indicating that acupoint catgut embedding therapy combined with moxibustion could be useful as a complementary therapy to reduce the risk of diabetes associated to obesity in women. Conclusion: Overall, our results confirmed the effectiveness of acupoints stimulation to assist in the control of body weight in women. They also highlighted the more favorable effects of embedded catgut-moxibustion combination that may be due to the extended and consistent stimulation of acupoints

ISOLDE PROGRAMME

The experiments aim at a broad exploration of the properties of atomic nuclei far away from the region of beta stability. Furthermore, the unique radioactive beams of over 60~elements produced at the on-line isotope separators ISOLDE-2 and ISOLDE-3 are used in a wide programme of atomic, solid state and surface physics. Around 300 scientists are involved in the project, coming from about 70 laboratories. \\ \\ The electromagnetic isotope separators are connected on-line with their production targets in the extracted 600 MeV proton or 910~MeV Helium-3 beam of the Synchro-Cyclotron. Secondary beams of radioactive isotopes are available at the facility in intensities of 10$^