A high performance liquid chromatography and electrospray ionization mass spectrometry method for the analysis of the natural medicine, Forsythia suspensa

Samples of Forsythia suspensa from raw (Laoqiao) and ripe (Qingqiao) fruit were analyzed with the use of HPLC-DAD and the EIS-MS techniques. Seventeen peaks were detected, and of these, twelve were identified. Most were related to the glucopyranoside molecular fragment. Samples collected from three geographical areas (Shanxi, Henan and Shandong Provinces), were discriminated with the use of hierarchical clustering analysis (HCA), discriminant analysis (DA), and principal component analysis (PCA) models, but only PCA was able to provide further information about the relationships between objects and loadings; eight peaks were related to the provinces of sample origin. The supervised classification models-K-nearest neighbor (KNN), least squares support vector machines (LS-SVM), and counter propagation artificial neural network (CP-ANN) methods, indicated successful classification but KNN produced 100% classification rate. Thus, the fruit were discriminated on the basis of their places of origin.

How should regulators deal with entrenched company executives?

The regulatory framework for corporate governance, both in Australia and internationally, shifts between rules based regimes and principles based approach. The rules based regimes are typified by legislation that imposes mandated compliance based rules, such as the Sarbanes Oxley Act. Other regimes, such as Australia’s CLERP 9 and the ASX Corporate Governance Council’s principles, have opted for a disclosure approach. This paper examines these approaches in the context of the non-binding vote rule, which arguably combines aspects of both. The study’s methodology empirically considers evidence relating to actual voting patterns as well as case study examples of the non-binding vote’s effectiveness. Significantly, our analyses show that from its inception, the non-binding vote was effective in motivating management to change the remuneration package to one they perceived as more acceptable to shareholders and that the non-binding vote is an effective regime to manage CEO remuneration (and by extension) executive remuneration.

A lightweight, modular robotic vehicle for the sustainable intensification of agriculture

This paper describes a lightweight, modular and energy efficient robotic vehicle platform designed for broadacre agriculture - the Small Robotic Farm Vehicle (SRFV). The current trend in farming is towards increasingly large machines that optimise the individual farmer’s productivity. Instead, the SRFV is designed to promote the sustainable intensification of agriculture by allowing farmers to concentrate on more important farm management tasks. The robot has been designed with a user-centred approach which focuses the outcomes of the project on the needs of the key project stakeholders. In this way user and environmental considerations for broadacre farming have informed the vehicle platform configuration, locomotion, power requirements and chassis construction. The resultant design is a lightweight, modular four-wheeled differential steer vehicle incorporating custom twin in-hub electric drives with emergency brakes. The vehicle is designed for a balance between low soil impact, stability, energy efficiency and traction. The paper includes modelling of the robot’s dynamics during an emergency brake in order to determine the potential for tipping. The vehicle is powered by a selection of energy sources including rechargeable lithium batteries and petrol-electric generators.

Supervised latent dirichlet allocation models for efficient activity representation

Local spatio-temporal features with a Bag-of-visual words model is a popular approach used in human action recognition. Bag-of-features methods suffer from several challenges such as extracting appropriate appearance and motion features from videos, converting extracted features appropriate for classification and designing a suitable classification framework. In this paper we address the problem of efficiently representing the extracted features for classification to improve the overall performance. We introduce two generative supervised topic models, maximum entropy discrimination LDA (MedLDA) and class- specific simplex LDA (css-LDA), to encode the raw features suitable for discriminative SVM based classification. Unsupervised LDA models disconnect topic discovery from the classification task, hence yield poor results compared to the baseline Bag-of-words framework. On the other hand supervised LDA techniques learn the topic structure by considering the class labels and improve the recognition accuracy significantly. MedLDA maximizes likelihood and within class margins using max-margin techniques and yields a sparse highly discriminative topic structure; while in css-LDA separate class specific topics are learned instead of common set of topics across the entire dataset. In our representation first topics are learned and then each video is represented as a topic proportion vector, i.e. it can be comparable to a histogram of topics. Finally SVM classification is done on the learned topic proportion vector. We demonstrate the efficiency of the above two representation techniques through the experiments carried out in two popular datasets. Experimental results demonstrate significantly improved performance compared to the baseline Bag-of-features framework which uses kmeans to construct histogram of words from the feature vectors.

Automatic UAV forced landing site detection using machine learning

The commercialization of aerial image processing is highly dependent on the platforms such as UAVs (Unmanned Aerial Vehicles). However, the lack of an automated UAV forced landing site detection system has been identified as one of the main impediments to allow UAV flight over populated areas in civilian airspace. This article proposes a UAV forced landing site detection system that is based on machine learning approaches including the Gaussian Mixture Model and the Support Vector Machine. A range of learning parameters are analysed including the number of Guassian mixtures, support vector kernels including linear, radial basis function Kernel (RBF) and polynormial kernel (poly), and the order of RBF kernel and polynormial kernel. Moreover, a modified footprint operator is employed during feature extraction to better describe the geometric characteristics of the local area surrounding a pixel. The performance of the presented system is compared to a baseline UAV forced landing site detection system which uses edge features and an Artificial Neural Network (ANN) region type classifier. Experiments conducted on aerial image datasets captured over typical urban environments reveal improved landing site detection can be achieved with an SVM classifier with an RBF kernel using a combination of colour and texture features. Compared to the baseline system, the proposed system provides significant improvement in term of the chance to detect a safe landing area, and the performance is more stable than the baseline in the presence of changes to the UAV altitude.

Attributing and denying humanness to others

We review a programme of research on the attribution of humanness to people, and the ways in which lesser humanness is attributed to some compared to others. We first present evidence that humanness has two distinct senses, one representing properties that are unique to our species, and the other—human nature—those properties that are essential or fundamental to the human category. An integrative model of dehumanisation is then laid out, in which distinct forms of dehumanisation correspond to the denial of the two senses of humanness, and the likening of people to particular kinds of nonhuman entities (animals and machines). Studies demonstrating that human nature attributes are ascribed more to the self than to others are reviewed, along with evidence of the phenomenon’s cognitive and motivational basis. Research also indicates that both kinds of humanness are commonly denied to social groups, both explicitly and implicitly, and that they may cast a new light on the study of stereotype content. Our approach to the study of dehumanisation complements the tradition of research on infrahumanisation, and indicates new directions for exploring the importance of humanness as a dimension of social perception.

Modeling and control of a 3500 tonne mining robot

Draglines are extremely large machines that are widely used in open-cut coal mines for overburden stripping. Since 1994 we have been working toward the development of a computer control system capable of automatically driving a dragline for a large portion of its operating cycle. This has necessitated the development and experimental evaluation of sensor systems, machines models, closed-loop control controllers, and an operator interface. This paper describes our steps toward the goal through scale-model and full-scale field experimentation.

Dragline modelling and control

Electric walking draglines are physically large and powerful machines used in the mining industry. However with the addition of suitable sensors and a controller a dragline can be considered as a numerically controlled machine or robot which can then perform parts of the operating cycle automatically. This paper presents an analysis of the electromechanical system necessary precursor to automatic control

Dragline swing automation

Draglines are very large machines that are used to remove overburden in open-cut coal mines. This paper outlines the design of a computer control system to implement an automated swing cycle on a production dragline. Subsystems and sensors have been developed to satisfy the constraints imposed by the task, the harsh operating environment and the mine's production requirements.

Case study: Dragline swing to dump automation

Dragline Swing to Dump Automation By Peter Corke, CSIRO Manufacturing Technology/CRC for Mining Technology and Equipment (CMTE) Peter Corke presented a case study of a project to automate the dragline swing to dump operation. The project is funded by ACARP, BHP Coal, Pacific Coal and the CMTE and is being carried out on a dragline at Pacific Coal's Meandu mine near Brisbane. Corke began by highlighting that the minerals industry makes extensive use of large, mechanised machines. However, unlike other industries, mining has not adopted automation and most machines are controlled by human operators on board the machine itself. Choosing an automation target The dragline automation was chosen because: ò draglines are one of the biggest capital assets in a mine; ò performance between operators vary significantly, so improved capital utilisation is possible; ò the dragline is often the bottleneck in production; ò a large part of the operation cycle is spent swinging from dig to dump; and ò it is technically feasible. There has been a history of drag line automation projects, none with great success.

Social media, audiences, and audience co-creativity in contemporary performance: Restoring balance?

Producers, technicians, performers, audiences and critics are all critical components of the performing arts ecology – critical components of an ecosystem that have to come together into some sort of productive relationship if the performing arts are to be vital, viable and successful. Different performance practices developed in different times, spaces and places do, of course, connect these players in different ways as part of their attempt to achieve their own definition of success, be it based on entertainment, educational, expression, empowerment, or something else. In some contemporary performance practices, social media platforms, applications and processes are seen to have significant potential to restore balance to the relationship between performer and audience, providing audiences with more power to participate in a performance event. In this paper, I investigate prevailing assumptions about social media’s power to democratise performance practice, or, at least, develop more co-creative performance practices in which producers, performers and audiences participate actively before, during and after the event. I focus, in particular, on the use of social media as a means of developing a participatory aesthetic in which an audience member is asked to contribute to the cast of characters, plot or progression of a performance. Although diverse – from performances streamed online, to performances that offer transmedia components the audience can use to learn more about character, context and plot online, to performances that incorporate online voting, liking or linking, to performances that unfold fully online on websites, blogs, microblogs or other social media platforms – what a lot of uses of social media in contemporary performance today share is a desire to encourage audiences to reflect on their role in making, and making meaning, of the event. In this paper I interrogate if, and if so how, this democratises or develops deeper levels of co-creativity in the relationship between producers, performers and audiences.

A comparative study of prognostic model for bearing failure

An effective prognostics program will provide ample lead time for maintenance engineers to schedule a repair and to acquire replacement components before catastrophic failures occur. This paper presents a technique for accurate assessment of the remnant life of machines based on health state probability estimation technique. For comparative study of the proposed model with the proportional hazard model (PHM), experimental bearing failure data from an accelerated bearing test rig were used. The result shows that the proposed prognostic model based on health state probability estimation can provide a more accurate prediction capability than the commonly used PHM in bearing failure case study.

The dawn of the age of the drones:An Australian privacy law perspective

Suppose a homeowner habitually enjoys sunbathing in his or her backyard, protected by a high fence from prying eyes, including those of an adolescent neighbour. In times past such homeowners could be assured that they might go about their activities without a threat to their privacy. However, recent years have seen technological advances in the development of unmanned aerial vehicles (‘UAVs’), also known colloquially as drones, that have allowed them to become more reduced in size, complexity and price. UAVs today include models retailing to the public for less than $350 and with an ease of operation that enables them to serve as mobile platforms for miniature cameras. These machines now mean that for individuals like the posited homeowner’s adolescent neighbour, barriers such as high fences no longer constitute insuperable obstacles to their voyeuristic endeavours. Moreover, ease of access to the internet and video sharing websites provides a ready means of sharing any recordings made with such cameras with a wide audience. Persons in the homeowner’s position might understandably seek some form of redress for such egregious invasions of their privacy. Other than some kind of self-help, what alternative measures may be available?

Bringing the farmer perspective to agricultural robots

The research reported in this paper explores autonomous technologies for agricultural farming application and is focused on the development of multiple-cooperative agricultural robots (AgBots). These are highly autonomous, small, lightweight, and unmanned machines that operate cooperatively (as opposed to a traditional single heavy machine) and are suited to work on broadacre land (large-scale crop operations on land parcels greater than 4,000m2). Since this is a new, and potentially disruptive technology, little is yet known about farmer attitudes towards robots, how robots might be incorporated into current farming practice, and how best to marry the capability of the robot with the work of the farmer. This paper reports preliminary insights (with a focus on farmer-robot control) gathered from field visits and contextual interviews with farmers, and contributes knowledge that will enable further work toward the design and application of agricultural robotics.

Unlearning leadership in the conceptual age

"We live in times in which unlearning has become as important as learning. Dan Pink has called these times the Conceptual Age,i to distinguish them from the Knowledge/Information Age in which many of us were born and educated. Before the current Conceptual Age, the core business of learning was the routine accessing of information to solve routine problems, so there was real value in retaining and reusing the templates taught to us at schools and universities. What is different about the Conceptual Age is that it is characterised by new cultural forms and modes of consumption that require us to unlearn our Knowledge/Information Age habits to live well in our less predictable social world. The ‘correct’ way to write, for example, is no longer ‘correct’ if communicating by hypertext rather than by essay or letter. And who would bother with an essay or a letter or indeed a pen these days? Whether or not we agree that the Conceptual Age, amounts to the first real generation gap since rock and roll, as Ken Robinson claims,ii it certainly makes unique demands of educators, just as it makes unique demands of the systems, strategies and sustainability of organisations. Foremost among these demands, according to innovation analyst Charlie Leadbeater,iii is to unlearn the idea that we are becoming a more knowledgeable society with each new generation. If knowing means being intimately familiar with the knowledge embedded in the technologies we use in our daily lives, then, Leadbeater says, we have never been more ignorant.iv He reminds us that our great grandparents had an intimate knowledge of the technologies around them, and had no problem with getting the butter churn to work or preventing the lamp from smoking. Few of us would know what to do if our mobile phones stopped functioning, just as few of us know what is ‘underneath’ or ‘behind’ the keys of our laptops. Nor, indeed, do many of us want to know. But this means that we are all very quickly reduced to the quill and the lamp if we lose our power sources or if our machines cease to function. This makes us much more vulnerable – as well as much more ignorant in relative terms – than our predecessors."