Monash University

About: Monash University is a education organization based out in Melbourne, Victoria, Australia. It is known for research contribution in the topics: Population & Poison control. The organization has 35920 authors who have published 100681 publications receiving 3027002 citations.

Conceptualizing a “Sustainability Business Model”:

Abstract: According to one perspective, organizations will only be sustainable if the dominant neoclassical model of the firm is transformed, rather than supplemented, by social and environmental priorities. This article seeks to develop a “sustainability business model” (SBM)—a model where sustainability concepts shape the driving force of the firm and its decision making. The SBM is drawn from two case studies of organizations considered to be leaders in operationalizing sustainability and is informed by the ecological modernization perspective of sustainability. The analysis reveals that organizations adopting a SBM must develop internal structural and cultural capabilities to achieve firm-level sustainability and collaborate with key stakeholders to achieve sustainability for the system that an organization is part of.

Efficient genetic markers for population biology.

Abstract: Population genetics has come of age. Three important components have come together: efficient techniques to examine informative segments of DNA, statistics to analyse DNA data and the availability of easy-to-use computer packages. Single-locus genetic markers and those that produce gene genealogies yield information that is truly comparable among studies. These markers answer biological questions most efficiently and also contribute to much broader investigations of evolutionary, population and conservation biology. For these reasons, single-locus and genealogical markers should be the focus of the intensive genetic data collection that has begun owing to the power of genetics in population biology.

Imaging beta-amyloid burden in aging and dementia.

Abstract: I read the article by Rowe et al.1 with interest. Diagnosing Alzheimer disease (AD) in a preclinical phase would enable early implementation of therapeutic interventions which might have long-term benefits. PET technology by using the Pittsburgh compound (PiB) allows detection of amyloid deposits in the brain.1,2 Pathologic studies indicate that amyloid deposition is present in cortical regions of all patients with AD even before the onset of dementia.3 One application of PiB-PET may be all that is necessary to identify the neuropathologic changes of AD in clinically normal individuals prior to the development of cognitive changes. These individuals would be considered to have preclinical AD. PET studies performed with PiB in cognitively intact subjects have shown that between 15 and 22% of them had abnormal scans.1,2 The objective is to determine who and when someone will become demented. It is and will be unclear because the proportion of subjects with cerebral amyloid deposits is, and will be, higher than the proportion of people with clinical AD. Otherwise, it is impossible to understand pathologic observations which indicate that more than 30% of older persons over age 75 die without any clinical evidence of dementia despite showing amyloid deposits and pathologic changes characteristic of AD.3 Presumably all of them would have abnormal PiB-PET scans. Amyloid deposition is not synonymous with clinical AD unless we assume that everyone with an abnormal PiB-PET scan would develop AD. Even in this case, many of them (70% according to pathologic data and prevalence estimates of AD) will die without dementia in the …

Mining data streams: a review

Abstract: The recent advances in hardware and software have enabled the capture of different measurements of data in a wide range of fields. These measurements are generated continuously and in a very high fluctuating data rates. Examples include sensor networks, web logs, and computer network traffic. The storage, querying and mining of such data sets are highly computationally challenging tasks. Mining data streams is concerned with extracting knowledge structures represented in models and patterns in non stopping streams of information. The research in data stream mining has gained a high attraction due to the importance of its applications and the increasing generation of streaming information. Applications of data stream analysis can vary from critical scientific and astronomical applications to important business and financial ones. Algorithms, systems and frameworks that address streaming challenges have been developed over the past three years. In this review paper, we present the state-of-the-art in this growing vital field.