Diving behaviour of albatrosses: implications for foraging ecology and bycatch susceptibility

Diving is an ecologically important behaviour that provides air-breathing predators with opportunities to capture prey, but that also increases their exposure to incidental mortality (bycatch) in commercial fisheries. In this study, we characterised the diving behaviour of 26 individuals of three species, the black-browed albatross Thalassarche melanophris, grey-headed albatross T. chrysostoma and light-mantled albatross Phoebetria palpebrata, breeding at Bird Island, South Georgia. Individuals were tracked using Global Location Sensor (GLS)-immersion loggers and time-depth recorders (TDRs) and, for two species, Global Positioning System (GPS) loggers. Although the TDRs recorded 589 dives (defined in this paper as submersion > 1 m), average dive depths and durations were just 1.30–1.49 m and 2.5–3.3 s, respectively, for the three species. In addition, many individuals (22% of black-browed, 20% of grey-headed, and 57% of light-mantled albatrosses; total n = 9, 10 and 7 individuals, respectively) did not dive at all. Most dives occurred at the distal end of foraging trips and were rare during the commuting phase. No dives took place in darkness, despite long periods spent on water at night. The limited and shallow dive activity contrasts with impressions from a previous study using capillary-tube depth gauges (which are less accurate than TDRs) and has implications for the susceptibility of albatrosses to bycatch on longlines. This study provides further support for regulations requiring night setting and increased sink rates of baited hooks to help mitigate albatross bycatch.

Ambient intelligence: Placement of Kinect sensors in the home of older adults with visual disabilities

BACKGROUND: Although a number of research studies on sensor technology for smart home environments have been conducted, there is still lack of consideration of human factors in implementing sensor technology in the home of older adults with visual disabilities. OBJECTIVE: This paper aims to advance knowledge of how sensor technology (e.g., Microsoft Kinect) should be implemented in the home of those with visual disabilities. METHODS: A convenience sample of 20 older adults with visual disabilities allowed us to observe their home environments and interview about the activities of daily living, which were analyzed via the inductive content analysis. RESULTS: Sensor technology should be integrated in the living environments of those with visual disabilities by considering various contexts, including people, tasks, tools, and environments (i.e., level-1 categories), which were further broken down into 22 level-2 categories and 28 level-3 categories. Each sub-category included adequate guidelines, which were also sorted by sensor location, sensor type, and data analysis. CONCLUSIONS: The guidelines will be helpful for researchers and professionals in implementing sensor technology in the home of older adults with visual disabilities.

Heuristics for Mixed Strength Sensor Location Problems

Location-detection problems are pervasive. Examples include the detection of faults in microprocessors, the identification of contaminants in ventilation systems, and the detection of illegal logging in rain forests. In each of these applications a network provides a convenient modelling paradigm. Sensors are placed at particular node locations that, by design, uniquely detect and locate issues in the network. Open locating-dominating (OLD) sets constrain a sensor's effectiveness by assuming that it is unable to detect problems originating from the sensor location. Sensor failures may be caused by extreme environmental conditions or by the act of a nefarious individual. Determining the minimum size OLD set in a network is computationally intractable, but can be modelled as an integer linear program. The focus of this work is the development and evaluation of heuristics for the minimum OLD set problem when sensors of varying strengths are allowed. Computational experience and solution quality are reported for geometric graphs of up to 150 nodes.

Energy Holes Minimization with Enhanced AEIAWSNHP Algorithm

The developments of wireless sensor network are motivated by many applications. It needs the Sensor nodes location. Sensor nodes are based primarily for identification procedure to resolve their significant position. In general, Sensor nodes are capable of some restricted power supply. As a result for detecting the power of sensor nodes an Identification algorithm is used by wireless sensor network. An Efficient Identify Algorithm for Wireless Sensor Networks with High Precession (AEIAWSNHP) is one efficient energy identification algorithm that has been proposed recently. In this work we examine the blow of using three techniques through the improvement of AEIAWSNHP in civilizing the energy efficient of enhanced AEIAWSNHP.At first, a Distinct-assessment Method, where a node estimate its location simply at one time. Secondly, active power manages; in this place the mention nodes decrease their communication power according to the gap to the node that transmits the position requirements. Third, an addition and expanding request speed method, that regulate the frequentness of dispatching the locate inquiry. The simulation result present that the new technique decreases the power utilization of the updated AEIAWSNH, Accuracy of the location assessment remains unchanged.