Non-optical biosensing in lab-on-a-chip systems

This paper presents a review of the recent trends and developments in non-optical biosensing platforms for lab-on- a-chip systems. This includes design considerations and applications of the non-optical biosensing platforms. The paper first categorizes the non-optical biosensors into four groups. The definition of each group together with a review of the reported works associated with the group are given. A performance analysis of different non-optical detection methods is also presented.

Optical micro analysis in lab-on-a-chips

Lab-on-a-chips are microfluidic devices which include biological laboratory functions on a single chip. They offer great advantages in terms of speed of analysis, precision, low sample and reagent consumption and automation of analysis. An efficient detection method in lab-on-a-chips is essential for the detection and quantification of chemical and biological parameters under examination. This review paper focuses on the recent research on optical detection techniques for LOC applications. Furthermore, several emerging detection paradigms which are still being matured are covered in this paper. Also, an analysis of the performance of different optical detection methods is given.

Parameter sensitivity analysis of surface plasmon resonance biosensor through numerical simulation

Surface based analytical tools have gained more importance for rapid, sensitive and label-free monitoring of molecular recognition events. Surface plasmon resonance (SPR) has played a prominent role in real time monitoring of surface binding events. SPR is increasing its significance especially for the study of ultrathin dielectric layer. This paper investigates the role of thin films of gold, silver and aluminium for protein detection in SPR biosensors. It is shown that the sensitivity, which is indicated by the shift of plasmon dip, is not linearly related to the thickness of protein but quadratic over a specific range. The approach involves a plot of a reflectivity curve as a function of the angle of incidence.

3-D modeling of a carbon nanotube cantilever biosensor

In this paper, 3-D finite element modeling and simulations are carried out to investigate the bending deformation of a single-walled carbon nanotube cantilever biosensor due to mass attached, and addition of a nano-scale particles to the beam tip resulting from the bioparticle detection. In addition, an algorithm for an electrostaticmechanical coupled system is developed. The computed results are in excellent agreement with the well known electrostatic equations that govern the deformation.

Social and environmental accounting research : major contributions and future directions for developing countries

A review is provided of major contributions in social and environmental accounting literature focussing on the issues of developing countries. The review of prior research shows that the major contributions have been related to the motivations for social and environmental disclosure. However, other important research areas such ethical/accountability issues and how to cost externalities which have already been considered within the context of developed countries are yet to emerge within the
developing country context. Contemporary social and environmental issues such as climate change and greenhouse gas emissions affecting the global community also appear to be key issues of research to scholars in both developed and developing countries. Finally, some future research directions are identified.

Media pressures and corporate disclosure of social responsibility performance information : a study of two global clothing and sports retail companies

This paper investigates the social and environmental disclosure practices of two large multinational companies, specifically Nike and Hennés & Mauritz. Utilising a joint consideration of legitimacy theory and media agenda setting theory, we investigate the linkage between negative media attention, and positive corporate social and environmental disclosures. Our results generally support a view that for those industry-related social and environmental issues attracting the greatest amount of negative media attention, these corporations react by providing positive social and environmental disclosures. The results were particularly significant in relation to labour practices in developing countries - the issue attracting the greatest amount of negative media attention for the companies in question.

PAPR reduction technique for LTE OFDMA systems

Long term evolution (LTE) is the final step toward the 4th generation (4G) of radio technologies designed to increase the capacity and speed of mobile networks. LTE uses orthogonal frequency division multiple access (OFDMA) for the downlink transmission and single carrier-frequency division multiple access (SC-FDMA) for uplink. OFDMA meets the 4G requirement for spectrum flexibility and enables cost-efficient solutions for very wide carriers with high peak rates. However, the potentially large peak-to-average power ratio (PAPR) of the transmitting signals has limited its application. This high PAPR causes interference when the OFDM signals are passed through an amplifier which does not have enough linear range. In this article, we investigate a clipping based PAPR reduction method for LTE OFDMA systems. Simulation results show that the clipping method is reduced PAPR significantly which decreases as the number of clip and filtering level is increased. As a results, increase the mean transmit power, and improve the power amplifier efficiency. This comes at the outlay of complexity, efficiency as well as cost.

Design and analysis of a cantilever biosensor based on a boron nitride nanotube

In this paper, we introduce a single-walled boron nitride nanotube (SWBNNT)-based cantilever biosensor, and investigate its bending deformation. The BNNT-based cantilever is modelled by accounting that the surface of the cantilever beam is coated with the antibody molecule. We have considered two main approaches for the mechanical deformation of the BNNT beam. The first one is differential surface stress produced by the binding of biomolecules onto its surface, and the second one is the charge released from the biomolecular interaction. In addition, other parameters including length of beam, variation of beam’s location and chiralities of the BNNT have been taken into consideration to design the cantilever biosensor. The computed results are in good agreement with the well known electrostatic equations that govern the deformation of the cantilever.

Corporate commitment to sustainability – is it all hot air? A review of the linkage between executive pay and sustainable performance

Across time, companies are increasingly making public commitments to sustainable development and to reducing their impacts on climate change. Management remuneration plans (MRPs) are a key mechanism to motivate managers to achieve corporate goals. We review the MRPs negotiated with key management personnel in a sample of large Australian carbon-intensive companies. Our results show that, as in past decades, the companies in our sample have MRPs in place that continue to fixate on financial performance. We argue that this provides evidence of a disconnection between the sustainability-related rhetoric of the sample companies, and their ‘real’ organisational priorities.

Orientalism and the Australian news media : origins and questions

The author draws on his expertise in Middle Eastern history and Western philosophical thought, presenting an ambitious chapter that traces the origins of orientalist discourse from the 18th and 19th century Europe to the beginnings of the Australian press. The chapter identifies the parallels between this discourse and the most contemporary representations of Islam and Muslims in the Australian news media. It documents that a key challenge for those working for the Australian news media is firstly to recognise the extent to which orientalist thought influences the reporting of Islam and Muslims and then to transcend the centuries-old view of this faith and its adherents as a threatening other.

The early Australian press and the Middle Eastern 'other'

In recent years, and particularly since the events of September 11 2001 and the subsequent “War on Terror”, much scholarly attention has been paid to the Australian news media’s role in stereotyping, homogenising, victimising and demonising people of Middle Eastern descent or of the Islamic faith. However, contemporary Australian journalists have not so much invented the tropes and stereotypes that they have used to construct this negative image and limited discursive field, as they have invoked a rich tapestry of pre-existing notions about the non-Western world. This paper therefore seeks to investigate the relationship between Edward Said’s notion of Orientalism and the Australian press of the late 19th and early 20th centuries. Beginning with its deplorable coverage of Australia’s Indigenous people and the paranoia surrounding the “Asian Invasion” this paper sheds new light on the coverage of Islam and the Middle East in the early Australian press and the emergence of the “Muslim Menace”. Finally, this chapter concludes by noting that such a racialist history raises a host of questions and challenges for the contemporary Australian news media.

Machine learning approaches for modeling spammer behavior

Spam is commonly known as unsolicited or unwanted email messages in the Internet causing potential threat to Internet Security. Users spend a valuable amount of time deleting spam emails. More importantly, ever increasing spam emails occupy server storage space and consume network bandwidth. Keyword-based spam email filtering strategies will eventually be less successful to model spammer behavior as the spammer constantly changes their tricks to circumvent these filters. The evasive tactics that the spammer uses are patterns and these patterns can be modeled to combat spam. This paper investigates the possibilities of modeling spammer behavioral patterns by well-known classification algorithms such as Naïve Bayesian classifier (Naive Bayes), Decision Tree Induction (DTI) and Support Vector Machines (SVMs). Preliminary experimental results demonstrate a promising detection rate of around 92%, which is considerably an enhancement of performance compared to similar spammer behavior modeling research.

Classification of malware based on string and function feature selection

Anti-malware software producers are continually challenged to identify and counter new malware as it is released into the wild. A dramatic increase in malware production in recent years has rendered the conventional method of manually determining a signature for each new malware sample untenable. This paper presents a scalable, automated approach for detecting and classifying malware by using pattern recognition algorithms and statistical methods at various stages of the malware analysis life cycle. Our framework combines the static features of function length and printable string information extracted from malware samples into a single test which gives classification results better than those achieved by using either feature individually. In our testing we input feature information from close to 1400 unpacked malware samples to a number of different classification algorithms. Using k-fold cross validation on the malware, which includes Trojans and viruses, along with 151 clean files, we achieve an overall classification accuracy of over 98%.

Differentiating malware from cleanware using behavioural analysis

This paper proposes a scalable approach for distinguishing malicious files from clean files by investigating the behavioural features using logs of various API calls. We also propose, as an alternative to the traditional method of manually identifying malware files, an automated classification system using runtime features of malware files. For both projects, we use an automated tool running in a virtual environment to extract API call features from executables and apply pattern recognition algorithms and statistical methods to differentiate between files. Our experimental results, based on a dataset of 1368 malware and 456 cleanware files, provide an accuracy of over 97% in distinguishing malware from cleanware. Our techniques provide a similar accuracy for classifying malware into families. In both cases, our results outperform comparable previously published techniques.