Drivers of variation in occurrence, abundance, and behaviour of sharks on coral reefs

Quantifying the drivers of population size in reef sharks is critical for the development of appropriate conservation strategies. In north-west Australia, shark populations inhabit coral reefs that border growing centres of human population, industry, and tourism. However, we lack baseline data on reef sharks at large spatial scales (hundreds of km) that might enable managers to assess the status of shark populations in the face of future development in this region. Here, we examined the occurrence, abundance and behaviour of apex (Galeocerdo cuvier, Carcharhinus plumbeus) and reef (C. amblyrhynchos, C. melanopterus, Triaenodon obesus) sharks using > 1200 deployments of baited remote underwater stereo-video systems (stereo-BRUVs) across > 500 km of coastline. We found evidence for species-specific influences of habitat and fishing activities on the occurrence (probability of observation), abundance (MaxN) and behaviour of sharks (time of arrival to the stereo-BRUVs and likelihood of feeding). Although the presence of management zoning (No-take areas) made little difference to most species, C. amblyrhynchos were more common further from boat ramps (a proxy of recreational fishing pressure). Time of arrival for all species was also influenced by distance to boat ramp, although patterns varied among species. Our results demonstrate the capacity for behavioural metrics to complement existing measures of occurrence and abundance in assessing the potential impact of human activities on shark populations.

Humans optimally anticipate and compensate for an uneven step during walking

The simple task of walking up a sidewalk curb is actually a dynamic prediction task. The curb is a disturbance that could cause a loss of momentum if not anticipated and compensated for. It might be possible to adjust momentum sufficiently to ensure undisturbed time of arrival, but there are infinite possible ways to do so. Much of steady, level gait is determined by energy economy, which should be at least as important with terrain disturbances. It is, however, unknown whether economy also governs walking up a curb, and whether anticipation helps. Here we show that humans compensate with an anticipatory pattern of forward speed adjustments, predicted by a criterion of minimizing mechanical energy input. The strategy is mechanistically predicted by optimal control for a simple model of bipedal walking dynamics, with each leg's push-off work as input. Optimization predicts a tri-phasic trajectory of speed (and thus momentum) adjustments, including an anticipatory phase. In experiment, human subjects ascend an artificial curb with the predicted tri-phasic trajectory, which approximately conserves overall walking speed relative to undisturbed flat ground. The trajectory involves speeding up in a few steps before the curb, losing considerable momentum from ascending it, and then regaining speed in a few steps thereafter. Descending the curb entails a nearly opposite, but still anticipatory, speed fluctuation trajectory, in agreement with model predictions that speed fluctuation amplitudes should scale linearly with curb height. The fluctuation amplitudes also decrease slightly with faster average speeds, also as predicted by model. Humans can reason about the dynamics of walking to plan anticipatory and economical control, even with a sidewalk curb in the way.

Interaction between Atlantic cyclones and Eurasian atmospheric blocking drives wintertime warm extremes in the high Arctic

Atmospheric blocking can influence Arctic weather by diverting the mean westerly flow and steering cyclones polewards, bringing warm, moist air to high latitudes. Recent studies have shown that diabatic heating processes in the ascending warm conveyor belt branch of extratropical cyclones are relevant to blocking dynamics. This leads to the question of the extent to which diabatic heating associated with mid-latitude cyclones may influence high-latitude blocking and drive Arctic warm events. In this study we investigate the dynamics behind 50 extreme warm events of wintertime high-Arctic temperature anomalies during 1979–2016. Classifying the warm events based on blocking occurrence within three selected sectors, we find that 30 of these events are associated with a block over the Urals, featuring negative upper-level potential vorticity (PV) anomalies over central Siberia north of the Ural Mountains. Lagrangian back-trajectory calculations show that almost 60 % of the air parcels making up these negative PV anomalies experience lifting and diabatic heating (median 11 K) in the 6 d prior to the block. Further, almost 70 % of the heated trajectories undergo maximum heating in a compact region of the mid-latitude North Atlantic, temporally taking place between 6 and 1 d before arriving in the blocking region. We also find anomalously high cyclone activity (on average five cyclones within this 5 d heating window) within a sector northwest of the main heating domain. In addition, 10 of the 50 warm events are associated with blocking over Scandinavia. Around 60 % of the 6 d back trajectories started from these blocks experience diabatic heating, of which 60 % undergo maximum heating over the North Atlantic but generally closer to the time of arrival in the block and further upstream relative to heated trajectories associated with Ural blocking. This study suggests that, in addition to the ability of blocks to guide cyclones northwards, Atlantic cyclones play a significant role in the dynamics of high-latitude blocking by providing low-PV air via moist-diabatic processes. This emphasizes the importance of the mutual interactions between mid-latitude cyclones and Eurasian blocking for wintertime Arctic warm extremes.

An eLoran Signal Cycle Identification Method Based on Joint Time–Frequency Domain

The eLoran system is an international standardized positioning, navigation, and timing service system, which can complement global navigation satellite systems to cope with navigation and timing warfare. The eLoran receiver measures time-of-arrival (TOA) through cycle identification, which is key in determining timing and positioning accuracy. However, noise and skywave interference can cause cycle identification errors, resulting in TOA-measurement errors that are integral multiples of 10 μs. Therefore, this article proposes a cycle identification method in the joint time–frequency domain. Based on the spectrum-division method to determine the cycle identification range, the time–domain peak-to-peak ratio and waveform matching are used for accurate cycle identification. The performance of the method is analyzed via simulation. When the signal-to-noise ratio (SNR) ≥ 0 dB and skywave-to-groundwave ratio (SGR) ≤ 23 dB, the success rate of cycle identification is 100%; when SNR ≥ −13 dB and SGR ≤ 23 dB, the success rate exceeds 75%. To verify its practicability, the method was implemented in the eLoran receiver and tested at three test sites within 1000 km using actual signals emitted by an eLoran system. The results show that the method has a high identification probability and can be used in modern eLoran receivers to improve TOA-measurement accuracy.

Timepix4, a large area pixel detector readout chip which can be tiled on 4 sides providing sub-200 ps timestamp binning

Timepix4 is a 24.7 × 30.0 mm2 hybrid pixel detector readout ASIC which has been designed to permit detector tiling on 4 sides. It consists of 448 × 512 pixels which can be bump bonded to a sensor with square pixels at a pitch of 55 µm. Like its predecessor, Timepix3, it can operate in data driven mode sending out information (Time of Arrival, ToA and Time over Threshold, ToT) only when a pixel has a hit above a pre-defined and programmable threshold. In this mode hits can be tagged to a time bin of <200 ps and Timepix4 can record hits correctly at incoming rates of ∼3.6 MHz/mm2/s. In photon counting (or frame-based) mode it can count incoming hits at rates of up to 5 GHz/mm2/s. In both modes data is output via between 2 and 16 serializers each running at a programmable data bandwidth of between 40 Mbps and 10 Gbps. The specifications, architecture and circuit implementation are described along with first electrical measurements and measurements with radioactive sources. In photon counting mode X-ray images have been taken at a threshold of 650 e− (with <10 masked pixels). In data driven mode images were taken of ToA/ToT data using a 90Sr source at a threshold of 800 e− (with ∼120 masked pixels).

The Efficiency of Drones Usage for Safety and Rescue Operations in an Open Area: A Case from Poland

The use of unmanned aerial systems (UAS) is becoming increasingly frequent during search and rescue (SAR) operations conducted to find missing persons. These systems have proven to be particularly useful for operations executed in the wilderness, i.e., in open and mountainous areas. The successful implementation of those systems is possible thanks to the potential offered by unmanned aerial vehicles (UAVs), which help achieve a considerable reduction in operational times and consequently allow a much quicker finding of lost persons. This is crucial to enhance their chances of survival in extreme conditions (withholding hydration, food and medicine, and hypothermia). The paper presents the results of a preliminary assessment of a search and rescue method conducted in an unknown terrain, where groups were coordinated with the use of UAVs and a ground control station (GCS) workstation. The conducted analysis was focused on assessing conditions that would help minimise the time of arrival of the rescue team to the target, which in real conditions could be a missing person identified on aerial images. The results of executed field tests have proven that the time necessary to reach injured persons can be substantially shortened if imaging recorded by UAV is deployed, as it considerably enhances the chance of survival in an emergency situation. The GCS workstation is also one of the crucial components in the search system, which assures image transmission from the UAV to participants of the search operation and radio signal amplification in a difficult terrain. The effectiveness of the search system was tested by comparing the arrival times of teams equipped with GPS and a compass and those not equipped with such equipment. The article also outlined the possibilities of extending the functionality of the search system with the SARUAV module, which was used to find a missing person in Poland.

On the Use of Deep Neural Networks to Improve Flights Estimated Time of Arrival Predictions

Predictable operations are the basis of efficient air traffic management. In this context, accurately estimating the arrival time to the destination airport is fundamental to make tactical decisions about an optimal schedule of landing and take-off operations. In this paper, we evaluate different deep learning models based on LSTM architectures for predicting estimated time of arrival of commercial flights, mainly using surveillance data from OpenSky Network. We observed that the number of previous states of the flight used to make the prediction have great influence on the accuracy of the estimation, independently of the architecture. The best model, with an input sequence length of 50, has reported a MAE of 3.33 min and a RMSE of 5.42 min on the test set, with MAE values of 5.67 and 2.13 min 90 and 15 min before the end of the flight, respectively.