Fine-tuning of unmanned aerial surveillance for ecological systems

Much of our understanding and management of ecological processes requires knowledge of the distribution and abundance of species. Reliable abundance or density estimates are essential for managing both threatened and invasive populations, yet are often challenging to obtain. Recent and emerging technological advances, particularly in unmanned aerial vehicles (UAVs), provide exciting opportunities to overcome these challenges in ecological surveillance. UAVs can provide automated, cost-effective surveillance and offer repeat surveys for pest incursions at an invasion front. They can capitalise on manoeuvrability and advanced imagery options to detect species that are cryptic due to behaviour, life-history or inaccessible habitat. UAVs may also cause less disturbance, in magnitude and duration, for sensitive fauna than other survey methods such as transect counting by humans or sniffer dogs. The surveillance approach depends upon the particular ecological context and the objective. For example, animal, plant and microbial target species differ in their movement, spread and observability. Lag-times may exist between a pest species presence at a site and its detectability, prompting a need for repeat surveys. Operationally, however, the frequency and coverage of UAV surveys may be limited by financial and other constraints, leading to errors in estimating species occurrence or density. We use simulation modelling to investigate how movement ecology should influence fine-scale decisions regarding ecological surveillance using UAVs. Movement and dispersal parameter choices allow contrasts between locally mobile but slow-dispersing populations, and species that are locally more static but invasive at the landscape scale. We find that low and slow UAV flights may offer the best monitoring strategy to predict local population densities in transects, but that the consequent reduction in overall area sampled may sacrifice the ability to reliably predict regional population density. Alternative flight plans may perform better, but this is also dependent on movement ecology and the magnitude of relative detection errors for different flight choices. Simulated investigations such as this will become increasingly useful to reveal how spatio-temporal extent and resolution of UAV monitoring should be adjusted to reduce observation errors and thus provide better population estimates, maximising the efficacy and efficiency of unmanned aerial surveys.

Automating Crop Damage Assessments with Unmanned Aerial Systems and Machine Learning

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising methodology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of this approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labelling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means clustering. The results show the algorithm delivers consistent decision boundaries that classify the field into three clusters, one for each crop health level as shown in Figure 1. The methodology presented in this paper represents a venue for further esearch towards automated crop damage assessments and biosecurity surveillance.

A model for mammalian male determination based on a passive Y chromosome

A model is suggested for mammalian male determination based on interactions postulated to occur among an autosomal repressor gene, an X-linked male-determining gene termed Tdx, and multiple copies of certain DNA sequences on the Y chromosome that do not code for any protein. The repressor, synthesised in limited amounts, has higher affinity for the Y-linked sequences than for Tdx and its affinity for Tdx is greater than that of RNA polymerase. In XY cells the Y effectively binds all available repressor, permitting transcription of Tdx to occur. In XX cells, since competition from the Y-linked high-affinity sequences is absent, the repressor binds to Tdx and prevents transcription. As a result of this competition between Tdx and the Y-linked high-affinity sites for limiting concentrations of the autosomal repressor, the product of the Tdx gene (TDX) is synthesized in the male but not in the female. It is suggested that in determination of the male sex, the role of the Y chromosome is to serve as a sink for the Tdx repressor. The proposed interactions provide a plausible explanation for the genetic properties of several anomalies of sexual development in mouse, man, and other mammals. The model suggests that the postulated multiple, highaffinity sequences on the Y chromosome of the mouse are included among the DNA sequences referred to as the Sxr-Bkm sequences.

Plan zemelʻnogo otvoda "Poltavt︠s︡y

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Zapys zemelʹnyĭ Na pravo korystovanni︠a︡ zemli︠u︡, shcho znakhoditʹsi︠a︡ v Odeskoï guberni Peryletravenʹsʹkyĭ okruzi Kantakuzenʹsʹy raʹoni pid nazvoi︠u︡ Trudovyshyĭ artilʹ Slava

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Zapys zemelʹnyĭ Na pravo korystovanni︠a︡ zemli︠u︡, shcho znakhoditʹsi︠a︡ v Odeskoï guberni Peryletravenʹsʹkyĭ okruzi Kantakuzenʹsʹy raʹoni pid nazvoi︠u︡ Trudovyshyĭ artilʹ Slava

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Transforming teaching practice through virtual classrooms: A study of staff and student views of synchronous learning

Australia is a vast land and access to quality higher education is challenging for many Australians that live outside the larger metropolitan areas. In 2010, the School of Education at an Australian university (Curtin University in Western Australia) moved to flexible delivery of a fully online Bachelor of Education degree for their rural students. The new model of delivery allows access for students from any location provided they have a computer and an internet connection.A number of teaching staff had previously used an asynchronous environment to deliver learning modules housed within a learning management system (LMS) but had not used synchronous software with their students. To enhance the learning environment and to provide high quality learning experiences to students learning at a distance, the adoption of synchronous software (Elluminate Live) was introduced. This software is a real-time virtual classroom environment that allows for communication through Voice over Internet Protocol (VoIP) and video conferencing, alongside a large number of collaboration tools to engage learners.This research paper reports on the integration of a live e-learning solution into the current Learning Management System (LMS) environment. Staff were interviewed about their perceptions and a questionnaire was administered to a sample of students to identify their experience with the synchronous software in order to inform future practice.

A Reactive Inverse PN Algorithm for Collision Avoidance among Multiple Unmanned Aerial Vehicles

While performing a mission, multiple Unmanned Aerial Vehicles (UAVs) need to avoid each other to prevent collisions among them. In this paper, we design a collision avoidance algorithm to resolve the conflict among UAVs that are on a collision course while flying to heir respective destinations. The collision avoidance algorithm consist of each UAV that is on a collision course reactively executing a maneuver that will, as in `inverse' Proportional Navigation (PN), increase Line of Sight (LOS) rate between them, resulting in a `pulling out' of collision course. The algorithm is tested for high density traffic scenarios as well as for robustness in the presence of noise.

Sonochemical Acceleration of Conversion of 2-Alkoxytetrahydrofurans to y-Butyrolactones Synthesis of (±)-Quercus Lactone-A

Effect of sonochemical irradiation on the conversion of 2-alkoxytetrahydrofurans to γ-butyro-1actores by Jones reagent, and its extension to the highly stereoselective synthesis of quercus lactone a, is reported.

A comparative molecular dynamics study of diffusion of n-decane and 3-methyl pentane in Y zeolite

Molecular dynamics simulations are reported on the structure and dynamics of n-decane and 3-methylpentane in zeolite NaY. We have calculated several properties such as the center of mass-center of mass rdf, the end-end distance distribution, bond angle distribution and dihedral angle distribution. We have also analysed trajectory to obtain diffusivity and velocity autocorrelation function (VACF). Surprisingly, the diffusivity of 3-methylpentane which is having larger cross-section perpendicular to the long molecular axis is higher than n-decane at 300 K. Activation energies have been obtained from simulations performed at 200 K, 300 K, 350 K, 400 K and 450 K in the NVE ensemble. These results can be understood in terms of the previously known levitation effect. Arrhenious plot has higher value of slope for n-decane (5 center dot 9 kJ/mol) than 3-methylpentane (3 center dot 7 kJ/mol) in agreement with the prediction of levitation effect.

Automatic wildfire detection and simulation using optical information from unmanned aerial systems

In many parts of the world, uncontrolled fires in sparsely populated areas are a major concern as they can quickly grow into large and destructive conflagrations in short time spans. Detecting these fires has traditionally been a job for trained humans on the ground, or in the air. In many cases, these manned solutions are simply not able to survey the amount of area necessary to maintain sufficient vigilance and coverage. This paper investigates the use of unmanned aerial systems (UAS) for automated wildfire detection. The proposed system uses low-cost, consumer-grade electronics and sensors combined with various airframes to create a system suitable for automatic detection of wildfires. The system employs automatic image processing techniques to analyze captured images and autonomously detect fire-related features such as fire lines, burnt regions, and flammable material. This image recognition algorithm is designed to cope with environmental occlusions such as shadows, smoke and obstructions. Once the fire is identified and classified, it is used to initialize a spatial/temporal fire simulation. This simulation is based on occupancy maps whose fidelity can be varied to include stochastic elements, various types of vegetation, weather conditions, and unique terrain. The simulations can be used to predict the effects of optimized firefighting methods to prevent the future propagation of the fires and greatly reduce time to detection of wildfires, thereby greatly minimizing the ensuing damage. This paper also documents experimental flight tests using a SenseFly Swinglet UAS conducted in Brisbane, Australia as well as modifications for custom UAS.

Recent progress on synthesis, multi-scale structure, and properties of Y-Si-O oxides

Yttrium silicates (Y-Si-O oxides), including Y2Si2O7, Y2SiO5, and Y4·67(SiO4)3O apatite, have attracted wide attentions from material scientists and engineers, because of their extensive polymorphisms and important roles as grain boundary phases in improving the high-temperature mechanical/thermal properties of Si3N4and SiC ceramics. Recent interest in these materials has been renewed by their potential applications as high-temperature structural ceramics, oxidation protective coatings, and environmental barrier coatings (EBCs). The salient properties of Y-Si-O oxides are strongly related to their unique chemical bonds and microstructure features. An in-depth understanding on the synthesis - multi-scale structure-property relationships of the Y-Si-O oxides will shine a light on their performance and potential applications. In this review, recent progress of the synthesis, multi-scale structures, and properties of the Y-Si-O oxides are summarised. First, various methods for the synthesis of Y-Si-O ceramics in the forms of powders, bulks, and thin films/coatings are reviewed. Then, the crystal structures, chemical bonds, and atomic microstructures of the polymorphs in the Y-Si-O system are summarised. The third section focuses on the properties of Y-Si-O oxides, involving the mechanical, thermal, dielectric, and tribological properties, their environmental stability, and their structure-property relationships. The outlook for potential applications of Y-Si-O oxides is also highlighted.

A novel ionic diffusion strategy to fabricate high-performance anode-supported solid oxide fuel cells (SOFCs) with proton-conducting Y-doped BaZrO3 films

A stable Y-doped BaZrO3 electrolyte film, which showed a good performance in proton-conducting SOFCs, was successfully fabricated using a novel ionic diffusion strategy.

Thermal properties and thermal shock resistance of γ-Y 2Si2O7

Thermal properties, namely, Debye temperature, thermal expansion coefficient, heat capacity, and thermal conductivity of γ-Y 2Si2O7, a high-temperature polymorph of yttrium disilicate, were investigated. The anisotropic thermal expansions of γ-Y2Si2O7 powders were examined using high-temperature X-ray diffractometer from 300 to 1373 K and the volumetric thermal expansion coefficient is (6.68±0.35) × 10-6 K-1. The linear thermal expansion coefficient of polycrystalline γ-Y2Si2O7 determined by push-rod dilatometer is (3.90±0.4) × 10-6 K-1, being very close to that of silicon nitride and silicon carbide. Besides, γ-Y2Si2O7 displays a low-thermal conductivity, with a κ value measured below 3.0 W·(m·K) -1 at the temperatures above 600 K. The calculated minimum thermal conductivity, κmin, was 1.35 W·(m·K) -1. The unique combination of low thermal expansion coefficient and low-thermal conductivity of γ-Y2Si2O7 renders it a very competitive candidate material for high temperature structural components and environmental/thermal-barrier coatings. The thermal shock resistance of γ-Y2Si2O7 was estimated by quenching dense materials in water from various temperatures and the critical temperature difference, ΔTc, was determined to be 300 K.