Unambiguous Turn Position and Rational Trace Languages

We show the existence of rational trace languages defined over direct products of free monoids that have inherent ambiguity of the order of log n and n 1/2 . This result is obtained by studying the relationship between trace languages and linear context-free grammars that satisfy a special unambiguity condition on the position of the last step of derivation

High-performance liquid chromatography analysis of mezlocillin, piperacillin, their degradation products, and of ioxitalamic acid in plasma and urine of healthy volunteers

In plasma and urine of 10 healthy volunteers after intravenous administration of 4 g mezlocillin and piperacillin, respectively, the parent compounds as well as degradation products were assayed by high-performance liquid chromatography. Ioxitalamic acid, a renal contrast medium, was administered simultaneously, in order to measure the glomerular filtration rate, and to control the collection of 24-h urine. As metabolite of mezlocillin the corresponding penicilloic acid only was found, whereas in the case of piperacillin a further degradation product was observed. Half of the doses given was recovered in the urine as unchanged drugs, and in addition 5-10% as metabolites. No differences were found in the pharmacokinetic behaviour of both antibiotics

Addressing environmental and atmospheric challenges for capturing high-precision thermal infrared data in the field of astro-ecology

Using thermal infrared detectors mounted on drones, and applying techniques from astrophysics, we hope to support the field of conservation ecology by creating an automated pipeline for the detection and identification of certain endangered species and poachers from thermal infrared data. We test part of our system by attempting to detect simulated poachers in the field. Whilst we find that we can detect humans hiding in the field in some types of terrain, we also find several environmental factors that prevent accurate detection, such as ambient heat from the ground, absorption of infrared emission by the atmosphere, obscuring vegetation and spurious sources from the terrain. We discuss the effect of these issues, and potential solutions which will be required for our future vision for a fully automated drone-based global conservation monitoring system.Comment: Published in Proceedings of SPIE Astronomical Telescopes and Instrumentation 2018. 8 pages, 3 figure

Counting crocodiles from the sky: Monitoring the critically endangered gharial (Gavialis gangeticus) population with an Unmanned Aerial Vehicle (UAV).

Technology is rapidly changing the methods in the field of wildlife monitoring. Unmanned aerial vehicle (UAV) is an example of a new technology that allows biologists to take to the air to monitor wildlife. Fixed Wing UAV was used to monitor critically endangered gharial population along 46 km of the Babai River in Bardia National Park. The UAV was flown at an altitude of 80 m along 12 pre-designed missions with a search effort of 2.72 hours of flight time acquired a total of 11,799 images covering an effective surface area of 8.2 km2 of river bank habitat. The images taken from the UAV could differentiate between gharial and muggers. A total count of 33 gharials and 31 muggers with observed density (per km2) of 4.64 and 4.0 for gharial and mugger respectively. Comparison of count data between one-time UAV and multiple conventional visual encounter rate surveys data showed no significant difference in the mean. Basking season and turbidity were important factors for monitoring crocodiles along the river bank habitat. Efficacy of monitoring crocodiles by UAV at the given altitude can be replicated in high priority areas with less operating cost and acquisition of high resolution data

Understanding External Influences on Target Detection and Classification Using Camera Trap Images and Machine Learning

Using machine learning (ML) to automate camera trap (CT) image processing is advantageous for time-sensitive applications. However, little is currently known about the factors influencing such processing. Here, we evaluate the influence of occlusion, distance, vegetation type, size class, height, subject orientation towards the CT, species, time-of-day, colour, and analyst performance on wildlife/human detection and classification in CT images from western Tanzania. Additionally, we compared the detection and classification performance of analyst and ML approaches. We obtained wildlife data through pre-existing CT images and human data using voluntary participants for CT experiments. We evaluated the analyst and ML approaches at the detection and classification level. Factors such as distance and occlusion, coupled with increased vegetation density, present the most significant effect on DP and CC. Overall, the results indicate a significantly higher detection probability (DP), 81.1%, and correct classification (CC) of 76.6% for the analyst approach when compared to ML which detected 41.1% and classified 47.5% of wildlife within CT images. However, both methods presented similar probabilities for daylight CT images, 69.4% (ML) and 71.8% (analysts), and dusk CT images, 17.6% (ML) and 16.2% (analysts), when detecting humans. Given that users carefully follow provided recommendations, we expect DP and CC to increase. In turn, the ML approach to CT image processing would be an excellent provision to support time-sensitive threat monitoring for biodiversity conservation

Optimising observing strategies for monitoring animals using drone-mounted thermal infrared cameras

The proliferation of relatively affordable off-the-shelf drones offers great opportunities for wildlife monitoring and conservation. Similarly the recent reduction in cost of thermal infrared cameras also offers new promise in this field, as they have the advantage over conventional RGB cameras of being able to distinguish animals based on their body heat and being able to detect animals at night. However, the use of drone-mounted thermal infrared cameras comes with several technical challenges. In this paper we address some of these issues, namely thermal contrast problems due to heat from the ground, absorption and emission of thermal infrared radiation by the atmosphere, obscuration by vegetation, and optimizing the flying height of drones for a best balance between covering a large area and being able to accurately image and identify animals of interest. We demonstrate the application of these methods with a case study using field data, and make the first ever detection of the critically endangered riverine rabbit (Bunolagus monticularis) in thermal infrared data. We provide a web-tool so that the community can easily apply these techniques to other studies (http://www.astro.ljmu.ac.uk/~aricburk/uav_calc/)

Thermal-Drones as a Safe and Reliable Method for Detecting Subterranean Peat Fires

Underground peat fires are a major hazard to health and livelihoods in Indonesia, and are a major contributor to carbon emissions globally. Being subterranean, these fires can be difficult to detect and track, especially during periods of thick haze and in areas with limited accessibility. Thermal infrared detectors mounted on drones present a potential solution to detecting and managing underground fires, as they allow large areas to be surveyed quickly from above and can detect the heat transferred to the surface above a fire. We present a pilot study in which we show that underground peat fires can indeed be detected in this way. We also show that a simple temperature thresholding algorithm can be used to automatically detect them. We investigate how different thermal cameras and drone flying strategies may be used to reliably detect underground fires and survey fire-prone areas. We conclude that thermal equipped drones are potentially a very powerful tool for surveying for fires and firefighting. However, more investigation is still needed into their use in real-life fire detection and firefighting scenarios

Spatial and temporal overlaps between leopards (Panthera pardus) and their competitors in the African large predator guild

Understanding the mechanisms facilitating coexistence within species assemblages is a key consideration for conservation as intact assemblages are necessary for maintaining full ecosystem function. The African large predator guild represents one of the few remaining functionally intact large predator assemblages on Earth, and as such, represents a unique study system to understand competitive interactions. Yet, relatively little is known of the coexistence mechanisms between some of its intermediately sized members, particularly leopards (Panthera pardus). Here, we use overlapping spatio‐temporal activity and GPS data on lions (Panthera leo), leopards, African wild dogs (Lycaon pictus) and cheetahs (Acinonyx jubatus) to examine spatial interactions and temporal partitioning between leopards and other guild members in northern Botswana. We found that at the population level, male leopard space use and activity patterns were largely unaffected by intraguild competitors. Leopards showed minimal movement coherence with competitors (avoidance or attraction) when moving through areas of home ranges shared with intraguild species. Moreover, we found evidence to support the hypothesis that guild species’ activity patterns are primarily driven by light availability rather than predator avoidance. Our results suggest predator avoidance has a limited impact on broad‐scale leopard spatio‐temporal niches, with aspects of the leopards’ ecology and life history likely facilitating its ability to thrive in close proximity to competitors. Considered alongside other studies, our results suggest that landscape‐level approaches to conservation may be suitable for aiding leopard conservation

Using Drones to Determine Chimpanzee Absences at the Edge of Their Distribution in Western Tanzania

Effective species conservation management relies on detailed species distribution data. For many species, such as chimpanzees (Pan troglodytes), distribution data are collected during ground surveys. For chimpanzees, such ground surveys usually focus on detection of the nests they build instead of detection of the chimpanzees themselves due to their low density. However, due to the large areas they still occur in, such surveys are very costly to conduct and repeat frequently to monitor populations over time. Species distribution models are more accurate if they include presence as well as absence data. Earlier studies used drones to determine chimpanzee presence using nests. In this study, therefore, we explored the use of drones to determine the absence of chimpanzee nests in areas we flew over on the edge of the chimpanzee distribution in western Tanzania. We conducted 13 flights with a fixed-wing drone and collected 3560 images for which manual inspection took 180 h. Flights were divided into a total of 746 25 m2 plots for which we determined the absence probability of nests. In three flights, we detected nests, in eight, absence was assumed based on a 95% probability criterion, and in two flights, nest absence could not be assumed. Our study indicates that drones can be used to cover relatively large areas to determine the absence of chimpanzees. To fully benefit from the usage of drones to determine the presence and absence of chimpanzees, it is crucial that methods are developed to automate nest detection in images