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A sequence of large earthquakes in central Italy ranging in moment magnitudes (Mw) from 4.2 to 6.5 caused significant damage to many small towns in the area. After each earthquake in 2016 (24 August and 26 October), automated small unmanned aerial vehicles (sUAV) acquired valuable imagery data for post-hazard reconnaissance in the mountain village of Pescara del Tronto, and were applied to 3D reconstruction using Structure-from-Motion (SfM). In July 2018, the site was again monitored to obtain additional imagery data capturing changes since the last visit following the 30 October 2016 Earthquake. A genetic-based mission-planning algorithm that delivers optimal viewpoints and path planning was field tested and reduced the required photos for 3D reconstruction by 9.1%. The optimized 3D model provides a better understanding of the current conditions of the village, when compared to the nadir models, by containing fewer holes on angled surfaces, including an additional 17% surface area, and with a comparable ground-sampling distance (GSD) of ≈2.4 cm/px (≈1.5 cm/px when adjusted for camera pixel density). The resulting three time-lapse models provide valuable metrics for ground motion, progression of damage, resilience of the village, and the recovery progress over a span of two years.

Jim Johnson sat in the windowless office of the Dayton Automated Flight Service Station at Dayton International Airport and recalled the good old days - 33 years of helping pilots get out of trouble or, better yet, avoid getting into it in the first place. Watching color images of weather maps flicker on computer screens, he recalled the time a pilot radioed in that she was lost somewhere around Oxford. Johnson retired in 1989, but he stopped by Tuesday to help the station celebrate its 65th birthday - and the 75th birthday of the nation's flight service system, which provides weather information and flight planning services for pilots.

This chapter contains sections titled: Introduction The UAS Operating Environment Sense and Avoid and Collision Avoidance Case Study: The Smart Skies Project Case Study: Flight Test Results Conclusion Acknowledgements References

Our ability to grab, hold, and manipulate objects involves our dexterous hands, our sense of touch, and feedback from our eyes and muscles that allows us to maintain a controlled grip. Billard and Kragic review the progress made in robotics to emulate these functions. Systems have developed from simple, pinching grippers operating in a fully defined environment, to robots that can identify, select, and manipulate objects from a random collection. Further developments are emerging from advances in computer vision, computer processing capabilities, and tactile materials that give feedback to the robot. Science , this issue p. eaat8414 Dexterous manipulation is one of the primary goals in robotics. Robots with this capability could sort and package objects, chop vegetables, and fold clothes. As robots come to work side by side with humans, they must also become human-aware. Over the past decade, research has made strides toward these goals. Progress has come from advances in visual and haptic perception and in mechanics in the form of soft actuators that offer a natural compliance. Most notably, immense progress in machine learning has been leveraged to encapsulate models of uncertainty and to support improvements in adaptive and robust control. Open questions remain in terms of how to enable robots to deal with the most unpredictable agent of all, the human.

Animals that depend on ephemeral, patchily distributed prey often use public information to locate resource patches. The use of public information can lead to the aggregation of foragers at prey patches, a mechanism known as local enhancement. However, when ephemeral resources are distributed over large areas, foragers may also need to increase search efficiency, and thus apply social strategies when sampling the landscape. While sensory networks of visually oriented animals have already been confirmed, we lack an understanding of how acoustic eavesdropping adds to the formation of sensory networks. Here we radio-tracked a total of 81 aerial-hawking bats at very high spatiotemporal resolution during five sessions over 3 y, recording up to 19 individuals simultaneously. Analyses of interactive flight behavior provide conclusive evidence that bats form temporary mobile sensory networks by adjusting their movements to neighboring conspecifics while probing the airspace for prey. Complementary agent-based simulations confirmed that the observed movement patterns can lead to the formation of mobile sensory networks, and that bats located prey faster when networking than when relying only on local enhancement or searching solitarily. However, the benefit of networking diminished with decreasing group size. The combination of empirical analyses and simulations elucidates how animal groups use acoustic information to efficiently locate unpredictable and ephemeral food patches. Our results highlight that declining local populations of social foragers may thus suffer from Allee effects that increase the risk of collapses under global change scenarios, like insect decline and habitat degradation.

Body condition (i.e. relative mass after correcting for structural size) affects the behaviour of migrating birds, but how body condition affects migratory performance, timing and fitness is still largely unknown. Here, we studied the effects of relative body condition on individual departure decisions, wind selectivity, flight speed and timing of migration for a long-distance migratory shorebird, the red knot Calidris canutus rufa. By using automated VHF telemetry on a continental scale, we studied knots' migratory movements with unprecedented temporal resolution over a 3-year period. Knots with a higher relative body condition left the staging site later than birds in lower condition, yet still arrived earlier to their Arctic breeding grounds compared to knots in lower relative body condition. They accomplished this by selecting more favourable winds at departure, thereby flying faster and making shorter stops en route. Individuals with a higher relative body condition in spring migrated south up to a month later than individuals in lower condition, suggesting that individuals in better condition were more likely to have bred successfully. Moreover, individuals with a lower relative body condition in spring had a lower probability of being detected in autumn, suggestive of increased mortality. The pressure to arrive early to the breeding grounds is considered to be an important constraint of migratory behaviour and this study highlights the important influence of body condition on migratory decisions, performance and potentially fitness of migrant birds.

• Volatile organic compounds (VOCs) play vital roles in the interaction of fungi with plants and other organisms. A systematic study of the global fungal VOC profiles is still lacking, though it is a prerequisite for elucidating the mechanisms of VOC-mediated interactions. Here we present a versatile system enabling a high-throughput screening of fungal VOCs under controlled temperature. In a proof-of-principle experiment, we characterized the volatile metabolic fingerprints of four Trichoderma spp. over a 48 h growth period. • The developed platform allows automated and fast detection of VOCs from up to 14 simultaneously growing fungal cultures in real time. The comprehensive analysis of fungal odors is achieved by employing proton transfer reaction-time of flight-MS and GC-MS. The data-mining strategy based on multivariate data analysis and machine learning allows the volatile metabolic fingerprints to be uncovered. • Our data revealed dynamic, development-dependent and extremely species-specific VOC profiles from the biocontrol genus Trichoderma. The two mass spectrometric approaches were highly complementary to each other, together revealing a novel, dynamic view to the fungal VOC release. • This analytical system could be used for VOC-based chemotyping of diverse small organisms, or more generally, for any in vivo and in vitro real-time headspace analysis.

In this review, we describe current Machine Learning approaches to hand gesture recognition with depth data from time-of-flight sensors. In particular, we summarise the achievements on a line of research at the Computational Neuroscience laboratory at the Ruhr West University of Applied Sciences. Relating our results to the work of others in this field, we confirm that Convolutional Neural Networks and Long Short-Term Memory yield most reliable results. We investigated several sensor data fusion techniques in a deep learning framework and performed user studies to evaluate our system in practice. During our course of research, we gathered and published our data in a novel benchmark dataset (REHAP), containing over a million unique three-dimensional hand posture samples.

The health impact of mosquito-borne diseases causes a huge burden on human societies. Recent vector control campaigns have resulted in promising declines in incidence and prevalence of these diseases, notably malaria, but resistance to insecticides and drugs are on the rise, threatening to overturn these gains. Moreover, several vector-borne diseases have re-emerged, requiring prompt and effective response measures. To improve and properly implement vector control interventions, the behaviour of the vectors must be well understood with detailed examination of mosquito flight being an essential component. Current knowledge on mosquito behaviour across its life history is briefly presented, followed by an overview of recent developments in automated tracking techniques for detailed interpretation of mosquito behaviour. These techniques allow highly accurate recording and observation of mating, feeding and oviposition behaviour. Software programmes built with specific algorithms enable quantification of these behaviours. For example, the crucial role of heat on host landing and the multimodal integration of carbon dioxide (CO 2 ) with other host cues, has been unravelled based on three-dimensional tracking of mosquito flight behaviour. Furthermore, the behavioural processes underlying house entry and subsequent host searching and finding can be better understood by analysis of detailed flight recordings. Further potential of these technologies to solve knowledge gaps is discussed. The use of tracking techniques can support or replace existing monitoring tools and provide insights on mosquito behaviour that can lead to innovative and more effective vector-control measures.

Radar is an effective tool for continuous monitoring and quantification of aerial bird movement and used to study migration and local flight behaviour. However, systems with automated tracking algorithms do not provide the level of processing sufficient to guarantee reliable data. Therefore, post‐processing such radar data is required but often non‐trivial, especially in challenging environments such as open sea. We present a post‐processing framework that implements knowledge of the radar system and bird biology to filter the data and retrieve reliable, high‐quality tracking data. The framework is split into three modules, each with a specific aim: (I) sub‐setting based on prior knowledge of the radar system and bird flight, (II) improving bird track quality and (III) detecting and removing spatio‐temporal sections of data that have a clear bias for false observations. The effectiveness of the framework is demonstrated with a case study comparing track densities inside and outside an offshore wind farm, and by applying the workflow to a dataset of visually validated radar tracks. Application of Module I resulted in a dataset of 520.894 bird tracks (19.5% of total data) within a 10.4 km 2 area. Additionally, 18.734 tracks were corrected for geometric errors in Module II, and Module III identified 236 of 719 observation hours and an area of 1.55 km 2 as unreliable for spatio‐temporal analysis. No difference in track densities was found between the area inside and outside the wind farm when using the post‐processed data, whereas using the unprocessed bird tracks, lower track densities were observed outside the wind farm. Of the visually validated radar tracks, the framework removed 85% of false positive bird tracks, while retaining 80% of true positive bird tracks. The framework provides a logical workflow to increase the reliability and quality of a bird radar dataset while being adaptable to the radar system and its surroundings. This is a first step towards standardising the post‐processing methodology for automated bird radar systems, which can facilitate comparative analyses of bird movement in space and time and improve the quality of ecological impact assessments.