Modeling the viability of the free-ranging cheetah population in Namibia : an object-oriented Bayesian network approach

Conservation of free-ranging cheetah (Acinonyx jubatus) populations is multi faceted and needs to be addressed from an ecological, biological and management perspective. There is a wealth of published research, each focusing on a particular aspect of cheetah conservation. Identifying the most important factors, making sense of various (and sometimes contrasting) findings, and taking decisions when little or no empirical data is available, are everyday challenges facing conservationists. Bayesian networks (BN) provide a statistical modeling framework that enables analysis and integration of information addressing different aspects of conservation. There has been an increased interest in the use of BNs to model conservation issues, however the development of more sophisticated BNs, utilizing object-oriented (OO) features, is still at the frontier of ecological research. We describe an integrated, parallel modeling process followed during a BN modeling workshop held in Namibia to combine expert knowledge and data about free-ranging cheetahs. The aim of the workshop was to obtain a more comprehensive view of the current viability of the free-ranging cheetah population in Namibia, and to predict the effect different scenarios may have on the future viability of this free-ranging cheetah population. Furthermore, a complementary aim was to identify influential parameters of the model to more effectively target those parameters having the greatest impact on population viability. The BN was developed by aggregating diverse perspectives from local and independent scientists, agents from the national ministry, conservation agency members and local fieldworkers. This integrated BN approach facilitates OO modeling in a multi-expert context which lends itself to a series of integrated, yet independent, subnetworks describing different scientific and management components. We created three subnetworks in parallel: a biological, ecological and human factors network, which were then combined to create a complete representation of free-ranging cheetah population viability. Such OOBNs have widespread relevance to the effective and targeted conservation management of vulnerable and endangered species.

One night CPAP withdrawal impairs performance at a driving simulator task faster than sleep restriction to 5 hours with treatment in OSA patients

Introduction Sleep restriction and missing 1 night’s continuous positive air pressure (CPAP) treatment are scenarios faced by obstructive sleep apnoea (OSA) patients, who must then assess their own fitness to drive. This study aims to assess the impact of this on driving performance. Method 11 CPAP treated participants (50–75 yrs), drove an interactive car simulator under monotonous motorway conditions for 2 hours on 3 afternoons, following;(i)normal night’s sleep (average 8.2 h) with CPAP (ii) sleep restriction (5 h), with CPAP (iii)normal length of sleep, without CPAP. Driving incidents were noted if the car came out of the designated driving lane. EEG was recorded continually and KSS reported every 200 seconds. Results Driving incidents: Incidents were more prevalent following CPAP withdrawal during hour 1, demonstrating a significant condition time interaction [F(6,60) = 3.40, p = 0.006]. KSS: At the start of driving participants felt sleepiest following CPAP withdrawal, by the end of the task KSS levels were similar following CPAP withdrawal and sleep restriction, demonstrating a significant condition, time interaction [F(3.94,39.41) = 3.39, p = 0.018]. EEG: There was a non significant trend for combined alpha and theta activity to be highest throughout the drive following CPAP withdrawal. Discussion CPAP withdrawal impairs driving simulator performance sooner than restricting sleep to 5 h with CPAP. Participants had insight into this increased sleepiness reflected by the higher KSS reported following CPAP withdrawal. In the practical terms of driving any one incident could be fatal. The earlier impairment reported here demonstrates the potential danger of missing CPAP treatment and highlights the benefit of CPAP treatment even when sleep time is short.

Modelling for the electricity distribution network

Global awareness for cleaner and renewable energy is transforming the electricity sector at many levels. New technologies are being increasingly integrated into the electricity grid at high, medium and low voltage levels, new taxes on carbon emissions are being introduced and individuals can now produce electricity, mainly through rooftop photovoltaic (PV) systems. While leading to improvements, these changes also introduce challenges, and a question that often rises is ‘how can we manage this constantly evolving grid?’ The Queensland Government and Ergon Energy, one of the two Queensland distribution companies, have partnered with some Australian and German universities on a project to answer this question in a holistic manner. The project investigates the impact the integration of renewables and other new technologies has on the physical structure of the grid, and how this evolving system can be managed in a sustainable and economical manner. To aid understanding of what the future might bring, a software platform has been developed that integrates two modelling techniques: agent-based modelling (ABM) to capture the characteristics of the different system units accurately and dynamically, and particle swarm optimization (PSO) to find the most economical mix of network extension and integration of distributed generation over long periods of time. Using data from Ergon Energy, two types of networks (3 phase, and Single Wired Earth Return or SWER) have been modelled; three-phase networks are usually used in dense networks such as urban areas, while SWER networks are widely used in rural Queensland. Simulations can be performed on these networks to identify the required upgrades, following a three-step process: a) what is already in place and how it performs under current and future loads, b) what can be done to manage it and plan the future grid and c) how these upgrades/new installations will perform over time. The number of small-scale distributed generators, e.g. PV and battery, is now sufficient (and expected to increase) to impact the operation of the grid, which in turn needs to be considered by the distribution network manager when planning for upgrades and/or installations to stay within regulatory limits. Different scenarios can be simulated, with different levels of distributed generation, in-place as well as expected, so that a large number of options can be assessed (Step a). Once the location, sizing and timing of assets upgrade and/or installation are found using optimisation techniques (Step b), it is possible to assess the adequacy of their daily performance using agent-based modelling (Step c). One distinguishing feature of this software is that it is possible to analyse a whole area at once, while still having a tailored solution for each of the sub-areas. To illustrate this, using the impact of battery and PV can have on the two types of networks mentioned above, three design conditions can be identified (amongst others): · Urban conditions o Feeders that have a low take-up of solar generators, may benefit from adding solar panels o Feeders that need voltage support at specific times, may be assisted by installing batteries · Rural conditions - SWER network o Feeders that need voltage support as well as peak lopping may benefit from both battery and solar panel installations. This small example demonstrates that no single solution can be applied across all three areas, and there is a need to be selective in which one is applied to each branch of the network. This is currently the function of the engineer who can define various scenarios against a configuration, test them and iterate towards an appropriate solution. Future work will focus on increasing the level of automation in identifying areas where particular solutions are applicable.

Constructing a clinical decision-making framework for image-guided radiotherapy using a Bayesian Network

A decision-making framework for image-guided radiotherapy (IGRT) is being developed using a Bayesian Network (BN) to graphically describe, and probabilistically quantify, the many interacting factors that are involved in this complex clinical process. Outputs of the BN will provide decision-support for radiation therapists to assist them to make correct inferences relating to the likelihood of treatment delivery accuracy for a given image-guided set-up correction. The framework is being developed as a dynamic object-oriented BN, allowing for complex modelling with specific sub-regions, as well as representation of the sequential decision-making and belief updating associated with IGRT. A prototype graphic structure for the BN was developed by analysing IGRT practices at a local radiotherapy department and incorporating results obtained from a literature review. Clinical stakeholders reviewed the BN to validate its structure. The BN consists of a sub-network for evaluating the accuracy of IGRT practices and technology. The directed acyclic graph (DAG) contains nodes and directional arcs representing the causal relationship between the many interacting factors such as tumour site and its associated critical organs, technology and technique, and inter-user variability. The BN was extended to support on-line and off-line decision-making with respect to treatment plan compliance. Following conceptualisation of the framework, the BN will be quantified. It is anticipated that the finalised decision-making framework will provide a foundation to develop better decision-support strategies and automated correction algorithms for IGRT.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Sensor Network Based AUV Localisation

The operation of Autonomous Underwater Vehicles (AUVs) within underwater sensor network fields provides an opportunity to reuse the network infrastructure for long baseline localisation of the AUV. Computationally efficient localisation can be accomplished using off-the-shelf hardware that is comparatively inexpensive and which could already be deployed in the environment for monitoring purposes. This paper describes the development of a particle filter based localisation system which is implemented onboard an AUV in real-time using ranging information obtained from an ad-hoc underwater sensor network. An experimental demonstration of this approach was conducted in a lake with results presented illustrating network communication and localisation performance.

GNSS ambiguity resolution with controllable failure rate for long baseline network RTK

Many large-scale GNSS CORS networks have been deployed around the world to support various commercial and scientific applications. To make use of these networks for real-time kinematic positioning services, one of the major challenges is the ambiguity resolution (AR) over long inter-station baselines in the presence of considerable atmosphere biases. Usually, the widelane ambiguities are fixed first, followed by the procedure of determination of the narrowlane ambiguity integers based on the ionosphere-free model in which the widelane integers are introduced as known quantities. This paper seeks to improve the AR performance over long baseline through efficient procedures for improved float solutions and ambiguity fixing. The contribution is threefold: (1) instead of using the ionosphere-free measurements, the absolute and/or relative ionospheric constraints are introduced in the ionosphere-constrained model to enhance the model strength, thus resulting in the better float solutions; (2) the realistic widelane ambiguity precision is estimated by capturing the multipath effects due to the observation complexity, leading to improvement of reliability of widelane AR; (3) for the narrowlane AR, the partial AR for a subset of ambiguities selected according to the successively increased elevation is applied. For fixing the scalar ambiguity, an error probability controllable rounding method is proposed. The established ionosphere-constrained model can be efficiently solved based on the sequential Kalman filter. It can be either reduced to some special models simply by adjusting the variances of ionospheric constraints, or extended with more parameters and constraints. The presented methodology is tested over seven baselines of around 100 km from USA CORS network. The results show that the new widelane AR scheme can obtain the 99.4 % successful fixing rate with 0.6 % failure rate; while the new rounding method of narrowlane AR can obtain the fix rate of 89 % with failure rate of 0.8 %. In summary, the AR reliability can be efficiently improved with rigorous controllable probability of incorrectly fixed ambiguities.

Preventing pollution attacks in multi-source network coding

Network coding is a method for achieving channel capacity in networks. The key idea is to allow network routers to linearly mix packets as they traverse the network so that recipients receive linear combinations of packets. Network coded systems are vulnerable to pollution attacks where a single malicious node floods the network with bad packets and prevents the receiver from decoding correctly. Cryptographic defenses to these problems are based on homomorphic signatures and MACs. These proposals, however, cannot handle mixing of packets from multiple sources, which is needed to achieve the full benefits of network coding. In this paper we address integrity of multi-source mixing. We propose a security model for this setting and provide a generic construction.

Synergism between Wnt3a and heparin enhances osteogenesis via a phosphoinositide 3-kinase/Akt/RUNX2 pathway

A new strategy has emerged to improve healing of bone defects using exogenous glycosaminoglycans by increasing the effectiveness of bone-anabolic growth factors. Wnt ligands play an important role in bone formation. However, their functional interactions with heparan sulfate/heparin have only been investigated in non-osseous tissues. Our study now shows that the osteogenic activity of Wnt3a is cooperatively stimulated through physical interactions with exogenous heparin. N-Sulfation and to a lesser extent O-sulfation of heparin contribute to the physical binding and optimal co-stimulation of Wnt3a. Wnt3a-heparin signaling synergistically increases osteoblast differentiation with minimal effects on cell proliferation. Thus, heparin selectively reduces the effective dose of Wnt3a needed to elicit osteogenic, but not mitogenic responses. Mechanistically, Wnt3a-heparin signaling strongly activates the phosphoinositide 3-kinase/Akt pathway and requires the bone-related transcription factor RUNX2 to stimulate alkaline phosphatase activity, which parallels canonical beta-catenin signaling. Collectively, our findings establish the osteo-inductive potential of a heparin-mediated Wnt3a-phosphoinositide 3-kinase/Akt-RUNX2 signaling network and suggest that heparan sulfate supplementation may selectively reduce the therapeutic doses of peptide factors required to promote bone formation.

A Cyber-Physical System Simulator for risk-free transport studies

The article introduces a novel platform for conducting controlled and risk-free driving and traveling behavior studies, called Cyber-Physical System Simulator (CPSS). The key features of CPSS are: (1) simulation of multiuser immersive driving in a threedimensional (3D) virtual environment; (2) integration of traffic and communication simulators with human driving based on dedicated middleware; and (3) accessibility of multiuser driving simulator on popular software and hardware platforms. This combination of features allows us to easily collect large-scale data on interesting phenomena regarding the interaction between multiple user drivers, which is not possible with current single-user driving simulators. The core original contribution of this article is threefold: (1) we introduce a multiuser driving simulator based on DiVE, our original massively multiuser networked 3D virtual environment; (2) we introduce OpenV2X, a middleware for simulating vehicle-to-vehicle and vehicle to infrastructure communication; and (3) we present two experiments based on our CPSS platform. The first experiment investigates the “rubbernecking” phenomenon, where a platoon of four user drivers experiences an accident in the oncoming direction of traffic. Second, we report on a pilot study about the effectiveness of a Cooperative Intelligent Transport Systems advisory system.

Mechanical influences on endothelial cell network formation in vitro

While both the restoration of the blood supply and an appropriate local mechanical environment are critical for uneventful bone healing, their influence on each other remains unclear. Human bone fracture haematomas (<72h post-trauma) were cultivated for 3 days in fibrin matrices, with or without cyclic compression. Conditioned medium from these cultures enhanced the formation of vessel-like networks by HMEC-1 cells, and mechanical loading further elevated it, without affecting the cells’ metabolic activity. While haematomas released the angiogenesis-regulators, VEGF and TGF-β1, their concentrations were not affected by mechanical loading. However, direct cyclic stretching of the HMEC-1 cells decreased network formation. The appearance of the networks and a trend towards elevated VEGF under strain suggested physical disruption rather than biochemical modulation as the responsible mechanism. Thus, early fracture haematomas and their mechanical loading increase the paracrine stimulation of endothelial organisation in vitro, but direct periodic strains may disrupt or impair vessel assembly in otherwise favourable conditions.

Introduction to the special issue on next generation network architectures, protocols, theory, systems and applications

This special issue of Networking Science focuses on Next Generation Network (NGN) that enables the deployment of access independent services over converged fixed and mobile networks. NGN is a packet-based network and uses the Internet protocol (IP) to transport the various types of traffic (voice, video, data and signalling). NGN facilitates easy adoption of distributed computing applications by providing high speed connectivity in a converged networked environment. It also makes end user devices and applications highly intelligent and efficient by empowering them with programmability and remote configuration options. However, there are a number of important challenges in provisioning next generation network technologies in a converged communication environment. Some preliminary challenges include those that relate to QoS, switching and routing, management and control, and security which must be addressed on an urgent or emergency basis. The consideration of architectural issues in the design and pro- vision of secure services for NGN deserves special attention and hence is the main theme of this special issue.

Proton-transfer and non-transfer compounds of the multi-purpose drug dapsone [4-(4-Aminophenylsulfonyl)aniline] with 3,5-dinitrosalicylic acid and 5-nitroisophthalic acid

The structures of the compounds from the reaction of the drug dapsone [4-(4-aminophenylsulfonyl)aniline] with 3,5-dinitrosalicylic acid, the salt hydrate [4-(4-aminohenylsulfonyl)anilinium 2-carboxy-4,6-dinitrophenolate monohydrate] (1) and the 1:1 adduct with 5-nitroisophthalic acid [4-(4-aminophenylsulfonyl)aniline 5-nitrobenzene-1,3-dicarboxylic acid] (2) have been determined. Crystals of 1 are triclinic, space group P-1, with unit cell dimensions a = 8.2043(3), b = 11.4000(6), c = 11.8261(6)Å, α = 110.891(5), β = 91.927(3), γ = 98.590(4)deg. and Z = 4. Compound 2 is orthorhombic, space group Pbcn, with unit cell dimensions a = 20.2662(6), b = 12.7161(4), c = 15.9423(5)Å and Z = 8. In 1, intermolecular analinium N-H…O and water O-H…O and O-H…N hydrogen-bonding interactions with sulfone, carboxyl, phenolate and nitro O-atom and aniline N-atom acceptors give a two-dimensional layered structure. With 2, the intermolecular interactions involve both aniline N-H…O and carboxylic acid O-H…O and O-H…N hydrogen bonds to sulfone, carboxyl, nitro and aniline acceptors, giving a three-dimensional network structure. In both structures π--π aromatic ring associations are present.

Assessment of driving-related performance in chronic whiplash using an advanced driving simulator

Driving is often nominated as problematic by individuals with chronic whiplash associated disorders (WAD), yet driving-related performance has not been evaluated objectively. The purpose of this study was to test driving-related performance in persons with chronic WAD against healthy controls of similar age, gender and driving experience to determine if driving-related performance in the WAD group was sufficiently impaired to recommend fitness to drive assessment. Driving-related performance was assessed using an advanced driving simulator during three driving scenarios; freeway, residential and a central business district (CBD). Total driving duration was approximately 15 min. Five driving tasks which could cause a collision (critical events) were included in the scenarios. In addition, the effect of divided attention (identify red dots projected onto side or rear view mirrors) was assessed three times in each scenario. Driving performance was measured using the simulator performance index (SPI) which is calculated from 12 measures. z-Scores for all SPI measures were calculated for each WAD subject based on mean values of the control subjects. The z-scores were then averaged for the WAD group. A z-score of ≤−2 indicated a driving failing grade in the simulator. The number of collisions over the five critical events was compared between the WAD and control groups as was reaction time and missed response ratio in identifying the red dots. Seventeen WAD and 26 control subjects commenced the driving assessment. Demographic data were comparable between the groups. All subjects completed the freeway scenario but four withdrew during the residential and eight during the CBD scenario because of motion sickness. All scenarios were completed by 14 WAD and 17 control subjects. Mean z-scores for the SPI over the three scenarios was statistically lower in the WAD group (−0.3 ± 0.3; P < 0.05) but the score was not below the cut-off point for safe driving. There were no differences in the reaction time and missed response ratio in divided attention tasks between the groups (All P > 0.05). Assessment of driving in an advanced driving simulator for approximately 15 min revealed that driving-related performance in chronic WAD was not sufficiently impaired to recommend the need for fitness to drive assessment.

Effects of wireless sensor network uncertainties on output-only modal analysis employing merged data of multiple tests

The use of Wireless Sensor Networks (WSNs) for vibration-based Structural Health Monitoring (SHM) has become a promising approach due to many advantages such as low cost, fast and flexible deployment. However, inherent technical issues such as data asynchronicity and data loss have prevented these distinct systems from being extensively used. Recently, several SHM-oriented WSNs have been proposed and believed to be able to overcome a large number of technical uncertainties. Nevertheless, there is limited research verifying the applicability of those WSNs with respect to demanding SHM applications like modal analysis and damage identification. Based on a brief review, this paper first reveals that Data Synchronization Error (DSE) is the most inherent factor amongst uncertainties of SHM-oriented WSNs. Effects of this factor are then investigated on outcomes and performance of the most robust Output-only Modal Analysis (OMA) techniques when merging data from multiple sensor setups. The two OMA families selected for this investigation are Frequency Domain Decomposition (FDD) and data-driven Stochastic Subspace Identification (SSI-data) due to the fact that they both have been widely applied in the past decade. Accelerations collected by a wired sensory system on a large-scale laboratory bridge model are initially used as benchmark data after being added with a certain level of noise to account for the higher presence of this factor in SHM-oriented WSNs. From this source, a large number of simulations have been made to generate multiple DSE-corrupted datasets to facilitate statistical analyses. The results of this study show the robustness of FDD and the precautions needed for SSI-data family when dealing with DSE at a relaxed level. Finally, the combination of preferred OMA techniques and the use of the channel projection for the time-domain OMA technique to cope with DSE are recommended.