ABSTRACT
The world's oceans are one of the most valuable sources of biodiversity and resources on the planet, although there are areas where the marine ecosystem is threatened by human activities. Marine protected areas (MPAs) are distinctive spaces protected by law due to their unique characteristics, such as being the habitat of endangered marine species. Even with this protection, there are still illegal activities such as poaching or anchoring that threaten the survival of different marine species. In this context, we propose an autonomous surface vehicle (ASV) model system for the surveillance of marine areas by detecting and recognizing vessels through artificial intelligence (AI)-based image recognition services, in search of those carrying out illegal activities. Cloud and edge AI computing technologies were used for computer vision. These technologies have proven to be accurate and reliable in detecting shapes and objects for which they have been trained. Azure edge and cloud vision services offer the best option in terms of accuracy for this task. Due to the lack of 4G and 5G coverage in offshore marine environments, it is necessary to use radio links with a coastal base station to ensure communications, which may result in a high response time due to the high latency involved. The analysis of on-board images may not be sufficiently accurate; therefore, we proposed a smart algorithm for autonomy optimization by selecting the proper AI technology according to the current scenario (SAAO) capable of selecting the best AI source for the current scenario in real time, according to the required recognition accuracy or low latency. The SAAO optimizes the execution, efficiency, risk reduction, and results of each stage of the surveillance mission, taking appropriate decisions by selecting either cloud or edge vision models without human intervention.
Subject(s)
Ecosystem , Robotics , Artificial Intelligence , Biodiversity , Conservation of Natural Resources , Humans , Oceans and SeasABSTRACT
Apart from their ecological value, the world's oceans are among the planet's most valuable resources, a rich source of food and wealth and in urgent need of protection. This article describes BUSCAMOS-RobObs, a robot-based observatory, consisting of an autonomous solar-powered marine robot with specialized sensing systems designed to carry out long-term observation missions in the inland sea of the Mar Menor in southeastern Spain. This highly specialised device is unique because it has the capacity to anchor itself to the seabed and become a "buoy", either to take measurements at specific points or to recharge its batteries. It thus avoids drifting and possible accidents in the buoy mode, especially near the coast, and resumes monitoring tasks when the required energy levels are reached. The robot is equipped with a broad range of sensors, including side scan sonar, sub-bottom sonar, laser systems, ultrasound sonar, depth meters, a multi-parametric probe and a GPS, which can collect georeferenced oceanic data. Although various types of autonomous vehicles have been described in the literature, they all have limited autonomy (even in the long term) as regards operational time and covering the seabed. The article describes a permanent monitoring mission in the Mar Menor, with a combination of solar energy and a decision-making strategy as regards the optimum route to be followed. The energy and mission simulation results, as well as an account of actual monitoring missions are also included.
ABSTRACT
La evaluación integral de factores de riesgo cardiovascular (FR) ha permitido diseñar tablas de estratificación de riesgo cardiovascular (RCV), que puedan predecir el riesgo global de sufrir un evento cardiovascular en los próximos 10 años en personas asintomáticas. Objetivo: Evaluar la prevalencia de FR en personas aparentemente sanas, y estratificarlas según su riesgo de presentar un evento cardiovascular en la próxima década. Métodos: Se estudiaron 12.409 personas sanas (PS), de 25 comunas de Santiago, edad 47,59 +-12 años, (51,5 porciento mujeres, 48,5 porciento hombres). Se evaluó conocimiento de FR, hábito tabáquico y alimentario (consumo de colesterol) actividad física y antecedentes familiares. Se midió presión arterial (PA), índice masa corporal (IMC, circunferencia cintura (CC), colesterol total (CT), y glicemia postprandial >4 hr (Gl). Se estratificaron en niveles de riesgo cardiovascular (RCV), según tablas de evaluación de RCV europeas, que consideran RCV bajo (<5 porciento), leve ( 5-10 porciento), moderado (10-20 porciento), alto (20-40 porciento)y muy alto (>40 porciento) de presentar un evento cardiovascular en los próximos 10 años. Resultados: la prevalencia de FR fue de 21,1 porciento hipertensión sistólica; 19,9 porciento hipertensión diastólica; 53,1 porciento hipercolesterolemia; 37,8 porciento tabaquismo; 68,6 porciento sedentarismo; 23,7 porciento obesidad, 71,1 porciento sobrepeso; y 5 porciento Gl alterada. El 22,8 porciento de la población tenía RCV entre 10 y 20 porciento y 5,6 porciento tenía RCV mayor a 20 porciento de tener un evento en los próximos 10 años. En los niveles de RCV >20 porciento, se encontró cifras significativamente mayores de PA, IMC, CC, CT, tabaquismo, sedentarismo y glicemia. Conclusiones: existe una alta prevalencia de FR. En el 28,4 porciento se encontró riesgo moderado/alto de presentar eventos cardiovasculares en los próximos 10 años. Este grupo presenta también otros FR no considerados en el modelo de estratificación.
