Comparative leaf anatomy and micromorphology of the Chilean Myrtaceae: Taxonomic and ecological implications

The family Myrtaceae in Chile comprises 26 species in 10 genera. The species occur in a diverse rangeof environments including humid temperate forests, swamps, riparian habitats and coastal xeromorphicshrublands. Most of these species are either endemic to Chile or endemic to the humid temperate forestsof Chile and Argentina. Although many taxa have very restricted distributions and are of conservationconcern, little is known about their biology and vegetative anatomy. In this investigation, we describe andcompare the leaf anatomy and micromorphology of all Chilean Myrtaceae using standard protocols forlight and scanning electron microscopy. Leaf characters described here are related to epidermis, cuticle,papillae, stomata, hairs, mesophyll, crystals, secretory cavities and vascular system. Nearly all the specieshave a typical mesophytic leaf anatomy, but some species possess xerophytic characters such as doubleepidermis, hypodermis, pubescent leaves, thick adaxial epidermis and straight epidermal anticlinal walls,which correlate with the ecological distribution of the species. This is the first report on leaf anatomyand micromorphology in most of these species. We identified several leaf characters with potential tax-onomic and ecological significance. Some combinations of leaf characters can reliably delimitate genera,while others are unique to some species. An identification key using micromorphological and anatomicalcharacters is provided to distinguish genera and species.

From vision to actions: Towards adaptive and autonomous humanoid robots

Although robotics research has seen advances over the last decades robots are still not in widespread use outside industrial applications. Yet a range of proposed scenarios have robots working together, helping and coexisting with humans in daily life. In all these a clear need to deal with a more unstructured, changing environment arises. I herein present a system that aims to overcome the limitations of highly complex robotic systems, in terms of autonomy and adaptation. The main focus of research is to investigate the use of visual feedback for improving reaching and grasping capabilities of complex robots. To facilitate this a combined integration of computer vision and machine learning techniques is employed. From a robot vision point of view the combination of domain knowledge from both imaging processing and machine learning techniques, can expand the capabilities of robots. I present a novel framework called Cartesian Genetic Programming for Image Processing (CGP-IP). CGP-IP can be trained to detect objects in the incoming camera streams and successfully demonstrated on many different problem domains. The approach requires only a few training images (it was tested with 5 to 10 images per experiment) is fast, scalable and robust yet requires very small training sets. Additionally, it can generate human readable programs that can be further customized and tuned. While CGP-IP is a supervised-learning technique, I show an integration on the iCub, that allows for the autonomous learning of object detection and identification. Finally this dissertation includes two proof-of-concepts that integrate the motion and action sides. First, reactive reaching and grasping is shown. It allows the robot to avoid obstacles detected in the visual stream, while reaching for the intended target object. Furthermore the integration enables us to use the robot in non-static environments, i.e. the reaching is adapted on-the- fly from the visual feedback received, e.g. when an obstacle is moved into the trajectory. The second integration highlights the capabilities of these frameworks, by improving the visual detection by performing object manipulation actions.

Performance-based contracting for roads – Experiences of Australia and Indonesia

Many countries over the last decade, have used performance-based contracting (PBC) to manage and maintain roads. The implementation of PBC provides additional benefits for the government/public such as cost savings and improved conditions of contracted road assets. In Australia, PBC is already being implemented on all categories of roads: national, state, urban and rural. Australian PBC arrangement is designed to turn over control and responsibility for roadway system maintenance, rehabilitation, and capital improvement projects to private contractors. Contractors’ responsibilities include determination of treatment types, the design, programming and the undertaking of works needed to maintain road networks at predetermined performance levels. Indonesia initiated two PBC pilot projects in 2011, the Pantura Section Demak-Trengguli (7.68 kilometers) in Central Java Province and Section Ciasem-Pamanukan (18.5 kilometers) in West Java Province. Both sections are categorized as national roads. The contract duration for both of these projects is four years. To facilitate a possible way forward, it is proposed to conduct a study to understand Australia's experiences of advancing from pilot projects to nation-wide programs using PBC. The study focuses on the scope of contracts, bidding processes, risk allocation, and key drivers, using relevant PBC case studies from Australia. Recommendations for future PBC deployment nation-wide should be based on more research associated with risk allocation. This will include investigation of standard conditions of contract. Implications of the contract clauses for the risk management strategy to be adopted by contractors. Based on the nature of risks, some are best managed by the project owner. It is very important that all parties involved to be open to the new rules of contract and to convince themselves about the potential increased benefits of the use of PBC. The most recent states of challenging issues were explored and described.

Choosing physical science courses: The importance of cultural and social capital in the enrolment decisions of high achieving students

This paper reports and discusses findings from a recent study which explored the science enrolment decisions of high achieving, or ‘science proficient’ secondary level students in Australia (Lyons 2003). The research was prompted by the increasing reluctance of such students to enrol in postcompulsory science courses, particularly in physics and chemistry. The study investigated the influences on students’ deliberations about taking a range of science courses. However, this report confines itself to decisions about enrolling in the physical sciences. The paper summarises the students’ experiences and conceptions of school science, as well as the characteristics of their ‘family worlds’ found to be influential in their decisions1. The paper discusses the important roles of cultural and social capital in these decisions, and concludes that enrolment in physical science courses was associated with congruence between the students’ conceptions of school science, and characteristics of their family backgrounds.

Enterprise technology governance: New information and technology core competencies for boards of directors

Boards of directors have legal and ethical responsibilities to be competent. Yet, in a world where business models and whole sectors are being disrupted by rapid information and technology change, a majority of directors lack IT governance knowledge and skills. Individual IT competency and collective board Enterprise Technology Governance capability is a global problem. Without capability, boards are potentially flying blind, and risk is increased and opportunities to lead and govern digital transformation lost. To address this capability gap, this research provides the first multi-industry validated Enterprise Technology Governance competency set for use in board evaluation, recruitment and professional development.

Risk assessment for airborne infectious diseases between natural ventilation and split-system air conditioner in a university classroom

Indoor air quality is a critical factor in the classroom due to high people concentration in a unique space. Indoor air pollutant might increase the chance of both long and short-term health problems among students and staff, reduce the productivity of teachers and degrade the student’s learning environment and comfort. Adequate air distribution strategies may reduce risk of infection in classroom. So, the purpose of air distribution systems in a classroom is not only to maximize conditions for thermal comfort, but also to remove indoor contaminants. Natural ventilation has the potential to play a significant role in achieving improvements in IAQ. The present study compares the risk of airborne infection between Natural Ventilation (opening windows and doors) and a Split-System Air Conditioner in a university classroom. The Wells-Riley model was used to predict the risk of indoor airborne transmission of infectious diseases such as influenza, measles and tuberculosis. For each case, the air exchange rate was measured using a CO2 tracer gas technique. It was found that opening windows and doors provided an air exchange rate of 2.3 air changes/hour (ACH), while with the Split System it was 0.6 ACH. The risk of airborne infection ranged between 4.24 to 30.86 % when using the Natural Ventilation and between 8.99 to 43.19% when using the Split System. The difference of airborne infection risk between the Split System and the Natural Ventilation ranged from 47 to 56%. Opening windows and doors maximize Natural Ventilation so that the risk of airborne contagion is much lower than with Split System.

Reducing luminance contrast on the window wall and users’ interventions in an office room

High luminance contrast between windows and surrounding surfaces could cause discomfort glare, which could reduce office workers’ productivity. It might also increase energy usage of buildings due to occupants’ interventions in lighting conditions to improve indoor visual quality. It is presumed that increasing the luminance of the areas surrounding the windows using a supplementary system, such Light Emitting Diodes (LEDs), could reduce discomfort glare. This paper reports on the results of a pilot study in a conventional office in Brisbane, Australia. The outcomes of this study indicated that a supplementary LED system could reduce the luminance contrast on the window wall from values in the order of 24:1 to 12:1. The results suggest that this reduction could significantly reduce discomfort glare from windows, as well as diminishing the likelihood of users’ intention to turn on the ceiling lights and/ or to move the blind down.

Using Bayesian networks to model surveillance in complex plant and animal health systems

In this chapter we consider biosecurity surveillance as part of a complex system comprising many different biological, environmental and human factors and their interactions. Modelling and analysis of surveillance strategies should take into account these complexities, and also facilitate the use and integration of the many types of different information that can provide insight into the system as a whole. After a brief discussion of a range of options, we focus on Bayesian networks for representing such complex systems. We summarize the features of Bayesian networks and describe these in the context of surveillance.

Decreasing luminance contrast on window walls as well as users’ interventions: Using LED lighting system

Mr Mehdi Amirkhani presented his research and case studies on improved lighting design in commercial spaces through daylight control and innovative lighting placement. This technical meeting was organised by IESANZ Queensland Chapter on April 7, 2016.

Place categorization and semantic mapping on a mobile robot

In this paper we focus on the challenging problem of place categorization and semantic mapping on a robot with-out environment-specific training. Motivated by their ongoing success in various visual recognition tasks, we build our system upon a state-of-the-art convolutional network. We overcome its closed-set limitations by complementing the network with a series of one-vs-all classifiers that can learn to recognize new semantic classes online. Prior domain knowledge is incorporated by embedding the classification system into a Bayesian filter framework that also ensures temporal coherence. We evaluate the classification accuracy of the system on a robot that maps a variety of places on our campus in real-time. We show how semantic information can boost robotic object detection performance and how the semantic map can be used to modulate the robot’s behaviour during navigation tasks. The system is made available to the community as a ROS module.