In situ observations of fish associated with coral reefs off Ireland

The abundance and behaviour of fish on and around coral reefs at Twin Mounds and Giant Mounds, carbonate mounds located on the continental shelf off Ireland (600-1100. m), were studied using two Remotely Operated Vehicle (ROV) dives. We recorded 30 fish taxa on the dives, together with three species of Scleractinia (Lophelia pertusa, Madrepora oculata and Desmophyllum cristagalli) and a diverse range of other corals (Antipatharia, Alcyonacea, and Stylasteridae). Stands of live coral provided the only habitat in which Guttigadus latifrons was observed whereas Neocyttus helgae was found predominantly on structural habitats provided by dead coral. Significantly more fish were found on structurally complex coral rubble habitats than on flatter areas where coral rubble was clogged with sand. The most common species recorded was Lepidion eques (2136 individuals), which always occurred a few cm above bottom and was significantly more active on the reefs than on sedimentary habitats. Synaphobranchus kaupii (1157 indiv.). , N. helgae (198 indiv.) and Micromesistius poutassou (116 indiv.) were also common; S. kaupii did not exhibit habitat-related differences in behaviour, whilst N. helgae was more active over the reefs and other structured habitats whereas M. poutassou was more active with decreasing habitat complexity. Trawl damage and abandoned fishing gear was observed at both sites. We conclude that Irish coral reefs provide complex habitats that are home to a diverse assemblage of fish utilising the range of niches occurring both above and within the reef structure. © 2011 Elsevier Ltd

The impact of predation by marine mammals on Patagonian toothfish longline fisheries

Predatory interaction of marine mammals with longline fisheries is observed globally, leading to partial or complete loss of the catch and in some parts of the world to considerable financial loss. Depredation can also create additional unrecorded fishing mortality of a stock and has the potential to introduce bias to stock assessments. Here we aim to characterise depredation in the Patagonian toothfish (Dissostichus eleginoides) fishery around South Georgia focusing on the spatio-temporal component of these interactions. Antarctic fur seals (Arctocephalus gazella), sperm whales (Physeter macrocephalus), and orcas (Orcinus orca) frequently feed on fish hooked on longlines around South Georgia. A third of longlines encounter sperm whales, but loss of catch due to sperm whales is insignificant when compared to that due to orcas, which interact with only 5% of longlines but can take more than half of the catch in some cases. Orca depredation around South Georgia is spatially limited and focused in areas of putative migration routes, and the impact is compounded as a result of the fishery also concentrating in those areas at those times. Understanding the seasonal behaviour of orcas and the spatial and temporal distribution of “depredation hot spots” can reduce marine mammal interactions, will improve assessment and management of the stock and contribute to increased operational efficiency of the fishery. Such information is valuable in the effort to resolve the human-mammal conflict for resources

Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions

In light of increasing alerts about limited energy sources and environment degradation, it has become essential to search for alternatives to thermal engine-based vehicles which are a major source of air pollution and fossil fuel depletion. Hybrid electric vehicles (HEVs), encompassing multiple energy sources, are a short-term solution that meets the performance requirements and contributes to fuel saving and emission reduction aims. Power management methods such as regulating efficient energy flow to the vehicle propulsion, are core technologies of HEVs. Intelligent power management methods, capable of acquiring optimal power handling, accommodating system inaccuracies, and suiting real-time applications can significantly improve the powertrain efficiency at different operating conditions. Rule-based methods are simply structured and easily implementable in real-time; however, a limited optimality in power handling decisions can be achieved. Optimization-based methods are more capable of achieving this optimality at the price of augmented computational load. In the last few years, these optimization-based methods have been under development to suit real-time application using more predictive, recognitive, and artificial intelligence tools. This paper presents a review-based discussion about these new trends in real-time optimal power management methods. More focus is given to the adaptation tools used to boost methods optimality in real-time. The contribution of this work can be identified in two points: First, to provide researchers and scholars with an overview of different power management methods. Second, to point out the state-of-the-art trends in real-time optimal methods and to highlight promising approaches for future development

Lifetime Model Development for Integration in Power Management of HEVs By Terms of Minimizing Fuel Consumption and Battery Degradation

Along with increasingly frequent use of electric and hybrid electric vehicles, the constraints and demands placed on them become stricter. The most noticeable challenge considering Hybrid Electric Vehicles (HEVs) is to provide an optimal power flow between multiple electric sources alongside provided as less as possible aging of energy storage components. To provide efficient battery usage with respect to battery life, it becomes unavoidable to develop battery lifetime models, which not only reflect the State-of-Heath (SoH) but also allow battery lifetime prediction. The lifetime-oriented battery models have to be integrated into power management. To be used efficiently and to provide optimal power split ensuring mitigation of battery degradation without sacrificing desired power consumption, accurate modeling of battery degradation is of utmost importance. This implies that gradual battery degradation, which is directly affected by applied loading profiles, has to be monitored and used as additional control input. Moreover, the lifetime model developed in this case has to provide model outputs also in the timeframe of power management. In this contribution, a machine state-based lifetime model for electric battery source was developed. In this particular case, different degradation states as well as machine state transitions are identified in accordance with current operating conditions. Here, the change in charge / discharge rate (C-rate), overcharging / undercharging of the battery (depth-of-discharge), and the temperature are taken into consideration to define machine model states. The End-of-Lifetime (EoL) is defined as the deviation between nominal and current ampere-hour (Ah) throughput. The proposed machine state-based lifetime model is verified based on existing battery lifetime models using simulation setup. The developed lifetime model in this way serves as a prerequisite for its integration into power management with an aim to provide the trade-off between aforementioned conflicting objectives; fuel consumption and battery degradation

A State-of-Health-Oriented Power Management Strategy for Multi-Source Electric Vehicles Considering Situation-Based Optimized Solutions in Real-Time

This paper presents a novel situation-based power and battery health management strategy for fuel cell vehicles. In such hybrid powertrains, the synergy role of batteries is essential to minimize overall power consumption and maintain higher electrical efficiency of the fuel cell. On the other hand, lifetime degradation of the battery is associated with the recurrent charging / discharging cycles. The proposed power management strategy addresses the trade-off between these contradictory objectives. Vehicle states in each situation are defined in terms of driver-related identification parameters (power demand and speed) corporately with powertrain related ones (on-board battery's state of charge). Optimal power handling solution for each situation is searched offline considering different optimization criteria: range extension, lifetime maximization, or power consumption minimization. A weighted fusion of these optimized solutions can be implemented online based on desired driving strategy, leading to situation-based optimized solution. This contribution aims to provide flexible power handling options meeting performance requirements (energy efficiency and driveability) without scarifying battery life. Simulation tests using different driving cycles are conducted for evaluation purpose