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.

Building networks to work: an ethnographic study of informal routes into the UK construction industry and pathways for migrant up-skilling

The UK construction industry labour market is characterised by high levels of self-employment, sub-contracting, informality and flexibility. A corollary of this, and a sign of the increasing globalisation of construction, has been an increasing reliance on migrant labour, particularly that from the Eastern European Accession states. Yet, little is known about how their experiences within and outside of work shape their work in the construction sector. In this context better qualitative understandings of the social and communication networks through which migrant workers gain employment, create routes through the sector and develop their role/career are needed. We draw on two examples from a short-term ethnographic study of migrant construction worker employment experiences and practices in the town of Crewe in Cheshire, UK, to demonstrate how informal networks intersect with formal elements of the sector to facilitate both recruitment and up-skilling. Such research knowledge, we argue, offers new evidence of the importance of attending to migrant worker’s own experiences in the development of more transparent recruitment processes.

The role of temperament and family environment in the development of anxiety disorder: two-year follow-up

Objective Behavioural inhibition (BI) in early childhood is associated with increased risk for anxiety. The present research examines BI alongside family environment factors, specifically maternal negativity and overinvolvement, maternal anxiety and mother-child attachment, with a view to providing a broader understanding of the development of child anxiety. Method Participants were 202 children classified at age 4 as either behaviourally inhibited (N=102) or uninhibited (N=100). Family environment, BI and child anxiety were assessed at baseline and child anxiety and BI were assessed again two-years later when participants were aged 6 years. Results After controlling for baseline anxiety, inhibited participants were significantly more likely to meet criteria for a diagnosis of social phobia and generalized anxiety disorder at follow-up. Path analysis suggested that maternal anxiety significantly affected child anxiety over time, even after controlling for the effects of BI and baseline anxiety. No significant paths from parenting or attachment to child anxiety were found. Maternal overinvolvement was significantly associated with BI at follow-up. Conclusions At age 4, BI, maternal anxiety and child anxiety represent risk factors for anxiety at age 6. Furthermore, overinvolved parenting increases risk for BI at age 6, which may then lead to the development of anxiety in later childhood.

Opening up climate research: A linked data approach to publishing data provenance

Traditionally, the formal scientific output in most fields of natural science has been limited to peer- reviewed academic journal publications, with less attention paid to the chain of intermediate data results and their associated metadata, including provenance. In effect, this has constrained the representation and verification of the data provenance to the confines of the related publications. Detailed knowledge of a dataset’s provenance is essential to establish the pedigree of the data for its effective re-use, and to avoid redundant re-enactment of the experiment or computation involved. It is increasingly important for open-access data to determine their authenticity and quality, especially considering the growing volumes of datasets appearing in the public domain. To address these issues, we present an approach that combines the Digital Object Identifier (DOI) – a widely adopted citation technique – with existing, widely adopted climate science data standards to formally publish detailed provenance of a climate research dataset as an associated scientific workflow. This is integrated with linked-data compliant data re-use standards (e.g. OAI-ORE) to enable a seamless link between a publication and the complete trail of lineage of the corresponding dataset, including the dataset itself.

Catch-up strategies in the Indian auto components industry: domestic firms’ responses to market liberalization

Market liberalization in emerging-market economies and the entry of multinational firms spur significant changes to the industry/institutional environment faced by domestic firms. Prior studies have described how such changes tend to be disruptive to the relatively backward domestic firms, and negatively affect their performance and survival prospects. In this paper, we study how domestic supplier firms may adapt and continue to perform, as market liberalization progresses, through catch-up strategies aimed at integrating with the industry's global value chain. Drawing on internalization theory and the literatures on upgrading and catch-up processes, learning and relational networks, we hypothesize that, for continued performance, domestic supplier firms need to adapt their strategies from catching up initially through technology licensing/collaborations and joint ventures with multinational enterprises (MNEs) to also developing strong customer relationships with downstream firms (especially MNEs). Further, we propose that successful catch-up through these two strategies lays the foundation for a strategy of knowledge creation during the integration of domestic industry with the global value chain. Our analysis of data from the auto components industry in India during the period 1992–2002, that is, the decade since liberalization began in 1991, offers support for our hypotheses.

Clusters, connectivity and catch-up: Bollywood and Bangalore in the Global Economy

In this article, we make two important contributions to the literature on clusters. First, we provide a broader theory of cluster connectivity that has hitherto focused on organization-based pipelines and MNE subsidiaries, by including linkages in the form of personal relationships. Second, we use the lens of social network theory to derive a number of testable propositions. We propose that global linkages with decentralized network structures have the highest potential for local spillovers. In the emerging economy context, our theory implies that clusters linked to the global economy by decentralized pipelines have potential for in-depth catch-up, focused in industry and technology scope. In contrast, clusters linked through decentralized personal relationships have potential for in-breadth catch-up over a range of related industries and technologies. We illustrate our theoretical propositions by contrasting two emerging economy case studies: Bollywood, the Indian filmed entertainment cluster in Mumbai and the Indian software cluster in Bangalore.