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* We examined vessel traffic threats on endangered killer whale foraging behavior. * Prey capture probability decreased as speed of proximate vessels increased. * Prey capture probability increased as estimates of preferred prey increased. * Vessel distance, noise and echosounders also influenced prey capture behavior. * Findings inform efforts to manage threats to endangered cetacean populations.

- Cross-border shipping of CO2 can play a key role in the Carbon Capture, Utilisation and Sequestration (CCUS) value chain, particularly when sequestration opportunities near emitters are limited. - By 2050, Asia Pacific (APAC) countries could require up to 150 vessels to transport an estimated 100 million tons per annum (MtPA) of captured CO2 across national borders. - Shipping is more cost-effective than offshore pipelines when transporting 5 MtPA or less of captured CO2 over a distance of 500 km or more.

Climate change poses a global challenge related to the reduction of pollutant atmospheric emissions and the maritime transportation sector is directly involved, due to its significant impact on the production of Greenhouse Gases and other substances. While established technologies have effectively targeted emissions like Nitrogen Oxides (NOX) and Sulfur Oxides (SOX), the persistence of Carbon dioxide (CO2) emissions represents an ongoing and significant concern. Novel technologies targeting CO2 reduction have been lately studied and proposed for inland applications, and are now being developed for maritime applications. With this regard, the present study explores the potential of Carbon Capture Systems (CCS) to mitigate CO2 emissions produced by cargo ships. While the implementation of CCS faces challenges, including space limitations and logistical complexities, its possible integration onboard marks a significant step in the fight against climate change. The authors propose an innovative approach using a Calcium Hydroxide Ca(OH)2 based CCS, offering the dual benefit of CO2 reduction and the potential resolution of ocean acidification through Calcium carbonate (CaCO3), the final product resulting from the CO2 capture process. Additionally, the study examines the feasibility of the generated product for reuse in industry, promoting a circular economy and addressing environmental issues. This innovative solution underscores the urgent need for transformative measures to reduce maritime emissions, in line with efforts to safeguarding the marine environment and combat climate change.

Pirates Capture Fourth Vessel Off Somalia.

Security Forces Capture 10 Somali Pirates in Stand-Off.

Chinese Forces May Have Captured Somali Pirate Leader.

Somali pirates capture Seychelles fishermen.

Coast Guard capturing more drug smuggling narco subs.

British capture of Iranian tanker won't go 'unanswered': officer.

Manly Ferry: Pictures capture Sydney Harbour service crashing through monster waves.

Pirates demand ransom for oil tanker captured off coast of Somalia.

Indonesia Captures Maritime Grave Robbers.

Crowley and Carbon Ridge Inc, a leading developer of modular onboard carbon capture and storage solutions (OCCS), with support from the U.S. Maritime Administration (MARAD) Maritime Environmental and Technical Assistance (META) program, have initiated an advanced, pilot project to reduce emissions impacts using Crowley's Storm international container ship.

Seabound said its system has the potential to capture 95% of emissions.

Indian navy captures 28 pirates from Thai vessel.

US commandos capture 4 suspected pirates in raid.

Indian navy captures 61 pirates in Arabian Sea.

Nigerian pirates threaten to kill captured Russian sailor.

Accurate and automated detection of maritime vessels present in aerial images is a considerable challenge. While significant progress has been made in recent years by adopting neural network architectures in detection and classification systems, these systems are usually designed specific to a sensor, dataset or location. In this paper, we present a system which uses multiple sensors and a convolutional neural network (CNN) architecture to test cross-sensor object detection resiliency. The system is composed of five main subsystems: Image Capture, Image Processing, Model Creation, Object-of-Interest Detection and System Evaluation. We show that the system has a high degree of cross-sensor vessel detection accuracy, paving the way for the design of similar systems which could prove robust across applications, sensors, ship types and ship sizes.