Adsorption from aqueous solutions on opened carbon nanotubes: organic compounds speed up delivery of water from inside

We report the results of first systematic studies of organic adsorption from aqueous solutions onto relatively long single walled carbon nanotubes (four tubes, in initial and oxidised forms). Using molecular dynamics simulations (GROMACS package) we discuss the behaviour of tube-water as well as tube-adsorbate systems, for three different adsorbates (benzene, phenol and paracetamol).

Bottom-up planning and the future of planning education in India

The article discusses normative guidelines for reorienting planning education in India within the context of the immensely influential Constitutional Amendment Act of 1993. First, it briefly sketches the status of planning education at present in India, in relation to the role of planners in planning practice. It then descibes the changes that have taken place in general, following the Constitutional Amendment Act, dwelling more on the specific changes within the State of Kerala. The implications of these for planning education in general are then discussed normatively, highlighting three areas that need immediate attention from the planning academic community.

Cognitive follow up of a small cohort of Huntington's disease patients over a 5 year period

A small group of patients with manifest Huntington's disease (HD) were followed longitudinally to assess cognitive decline in relation to time from disease diagnosis. This article looks at performance on a range of computerised and pencil and paper cognitive tasks in patients 5 years post diagnosis, who were assessed annually for a 5 year follow up period. The almost universal cognitive decline reported in other longitudinal studies of HD was not replicated in this study. It was proposed that longitudinal follow up in HD is complicated by the varying degree to which different tasks are able to withstand repeated administration; a finding which would have significant implications on study design in future trials of cognitive enhansing interventions.

Solar cycle 24: what is the sun up to?

March 2012 brought the first solar and geomagnetic disturbances of any note during solar cycle 24. But perhaps what was most remarkable about these events was how unremarkable they were compared to others during the space-age, attracting attention only because solar activity had been so quiet. This follows an exceptionally low and long-lived solar cycle minimum, and so the current cycle looks likely to extend a long-term decline in solar activity that started around 1985 and that could even lead to conditions similar to the Maunder minimum within 40 years from now, with implications for solar-terrestrial science and the mitigation of space weather hazards and maybe even for climate in certain regions and seasons.

Look up, look around: is there anything different about team-level OCB in China?

Ethical leadership has been widely identified as the key variable in enhancing team-level organizational citizenship behavior (team-level OCB) in western economic and business contexts. This is challenged by empirical evidence in China and findings of this study. Our study examined the relationship between ethical leadership, organizational ethical context (ethical culture and corporate ethical values) and team-level OCB. Team-level data has been collected from 57 functional teams in 57 firms operating in China. The findings suggest that although ethical leadership is positively associated with team-level OCB, ethical context positively moderates the relationship between ethical leadership and team-level OCB. The higher ethical context is found to be, the greater is the (positive) effects of ethical leadership on team-level OCB and the opposite holds true when ethical context is low. Key implications are discussed on the role of contextual ethics for team level OCB, while managerial implications include how non-Chinese firms could improve team-level OCB in the Chinese business context.

P-Prism: a computationally efficient approach to scaling up classification rule induction

Top Down Induction of Decision Trees (TDIDT) is the most commonly used method of constructing a model from a dataset in the form of classification rules to classify previously unseen data. Alternative algorithms have been developed such as the Prism algorithm. Prism constructs modular rules which produce qualitatively better rules than rules induced by TDIDT. However, along with the increasing size of databases, many existing rule learning algorithms have proved to be computational expensive on large datasets. To tackle the problem of scalability, parallel classification rule induction algorithms have been introduced. As TDIDT is the most popular classifier, even though there are strongly competitive alternative algorithms, most parallel approaches to inducing classification rules are based on TDIDT. In this paper we describe work on a distributed classifier that induces classification rules in a parallel manner based on Prism.

Scaling up classification rule induction through parallel processing

The fast increase in the size and number of databases demands data mining approaches that are scalable to large amounts of data. This has led to the exploration of parallel computing technologies in order to perform data mining tasks concurrently using several processors. Parallelization seems to be a natural and cost-effective way to scale up data mining technologies. One of the most important of these data mining technologies is the classification of newly recorded data. This paper surveys advances in parallelization in the field of classification rule induction.

Scaling up data mining techniques to large datasets using parallel and distributed processing

Advances in hardware and software technology enable us to collect, store and distribute large quantities of data on a very large scale. Automatically discovering and extracting hidden knowledge in the form of patterns from these large data volumes is known as data mining. Data mining technology is not only a part of business intelligence, but is also used in many other application areas such as research, marketing and financial analytics. For example medical scientists can use patterns extracted from historic patient data in order to determine if a new patient is likely to respond positively to a particular treatment or not; marketing analysts can use extracted patterns from customer data for future advertisement campaigns; finance experts have an interest in patterns that forecast the development of certain stock market shares for investment recommendations. However, extracting knowledge in the form of patterns from massive data volumes imposes a number of computational challenges in terms of processing time, memory, bandwidth and power consumption. These challenges have led to the development of parallel and distributed data analysis approaches and the utilisation of Grid and Cloud computing. This chapter gives an overview of parallel and distributed computing approaches and how they can be used to scale up data mining to large datasets.