Predation risk is a major driver of the distribution of prey animals, which typically show strong responses to cues for predator presence. An unresolved question is whether naïve individuals respond to mimicked cues, and whether such cues can be used to deter prey. We investigated whether playback of wolf sounds induces fear responses in naïve ungulates in a human-dominated landscape from which wolves have been eradicated since 1879. We conducted a playback experiment in mixed-coniferous and broadleaved forest that harboured three cervid and one suid species. At 36 locations, we played wolf sounds, sounds of local sheep or no sounds, consecutively, in random order, and recorded visit rate and group size, using camera traps. Visit rates of cervids and wild boar showed a clear initial reduction to playback of both wolf and sheep sounds, but the type of response differed between sound, forest type and species. For naïve wild boar in particular, responses to predator cues depended on forest type. Effects on visit rate disappeared within 21 days. Group sizes in all the species were not affected by the sound treatment. Our findings suggest that the responses of naïve ungulates to wolf sound seem to be species specific, depend on forest type and wear off in time, indicating habituation. Before we can successfully deter ungulates using predator sound, we should further investigate how different forest types affect the perception of naïve ungulates to these sounds, as responses to predator sound may depend on habitat characteristics.
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Mechanical power output is a key performance-determining variable in many cyclic sports. In rowing, instantaneous power output is commonly determined as the dot product of handle force moment and oar angular velocity. The aim of this study was to show that this commonly used proxy is theoretically flawed and to provide an indication of the magnitude of the error. To obtain a consistent dataset, simulations were performed using a previously proposed forward dynamical model. Inputs were previously recorded rower kinematics and horizontal oar angle, at 20 and 32 strokes∙min−1. From simulation outputs, true power output and power output according to the common proxy were calculated. The error when using the common proxy was quantified as the difference between the average power output according to the proxy and the true average power output (P̅residual), and as the ratio of this difference to the true average power output (ratiores./rower). At stroke rate 20, P̅residual was 27.4 W and ratiores./rower was 0.143; at stroke rate 32, P̅residual was 44.3 W and ratiores./rower was 0.142. Power output in rowing appears to be underestimated when calculated according to the common proxy. Simulations suggest this error to be at least 10% of the true power output.
This letter to the editor argues that the assumption of a single value for the acetate recovery factor in carbon-13 stable isotope research for different individuals, can lead to significant errors in the outcomes of substrate utilization measurements.
In this project, the AGM R&D team developed and refined the use of a facial scanning rig. The rig is a physical device comprising multiple cameras and lighting that are mounted on scaffolding around a 'scanning volume'. This is an area at which objects are placed before being photographed from multiple angles. The object is typically a person's head, but it can be anything of this approximate size. Software compares the photographs to create a digital 3D recreation - this process is called photogrammetry. The 3D model is then processed by further pieces of software and eventually becomes a face that can be animated inside in Unreal Engine, which is a popular piece of game development software made by the company Epic. This project was funded by Epic's 'Megagrant' system, and the focus of the work is on streamlining and automating the processing pipeline, and on improving the quality of the resulting output. Additional work has been done on skin shaders (simulating the quality of real skin in a digital form) and the use of AI to re/create lifelike hair styles. The R&D work has produced significant savings in regards to the processing time and the quality of facial scans, has produced a system that has benefitted the educational offering of BUas, and has attracted collaborators from the commercial entertainment/simulation industries. This work complements and extends previous work done on the VIBE project, where the focus was on creating lifelike human avatars for the medical industry.
Het doel van dit interdisciplinaire SIA KIEM project Fluïde Eigenschap in de Creatieve Industrie is te onderzoeken of en hoe gedeelde vormen van eigenaarschap in de creatieve industrie kunnen bijdragen aan het creëren van een democratischer en duurzamer economie, waarin ook het MKB kan participeren in digitale innovatie. Het project geeft een overzicht van beschikbare vormen van (gedeeld) eigenaarschap, hun werking en hoe deze creatieve professionals kunnen ondersteunen bij de transitie naar de platformeconomie. Dit wordt toegepast op een concrete case, dat van een digitale breimachine. Naast het leveren van een goede praktijk, moet het project leiden tot een groter internationaal onderzoeksvoorstel over Fluid Ownership in the Creative Industry, dat dieper ingaat op de beschikbare eigendomsoplossingen en hoe deze waarde zullen creëren voor de creatieve professional.
Due to the exponential growth of ecommerce, the need for automated Inventory management is crucial to have, among others, up-to-date information. There have been recent developments in using drones equipped with RGB cameras for scanning and counting inventories in warehouse. Due to their unlimited reach, agility and speed, drones can speed up the inventory process and keep it actual. To benefit from this drone technology, warehouse owners and inventory service providers are actively exploring ways for maximizing the utilization of this technology through extending its capability in long-term autonomy, collaboration and operation in night and weekends. This feasibility study is aimed at investigating the possibility of developing a robust, reliable and resilient group of aerial robots with long-term autonomy as part of effectively automating warehouse inventory system to have competitive advantage in highly dynamic and competitive market. To that end, the main research question is, “Which technologies need to be further developed to enable collaborative drones with long-term autonomy to conduct warehouse inventory at night and in the weekends?” This research focusses on user requirement analysis, complete system architecting including functional decomposition, concept development, technology selection, proof-of-concept demonstrator development and compiling a follow-up projects.
