Corporate social responsibility in construction and engineering firms: key elements of effective strategy

Through multiple case studies of firms we argue that firms that have developed corporate responsibility strategies, albeit informally at first, do so by making intentional, informed and collective choices about CSR initiatives. More precisely, we point to the importance of considering corporate identity in making these choices and to the process of adaptive coordination, which includes both responding to and influencing the CSR environment. We conclude that CSR strategic landscape are determined more and more by the astute and careful management of a network of cooperative and competitive stakeholder interests which possess both tangible and intangible value to a firm.

Incorporation of water vapour transfer in the JULES Land Surface Model: implications for key soil variables and land surface fluxes

This study focuses on the mechanisms underlying water and heat transfer in upper soil layers, and their effects on soil physical prognostic variables and the individual components of the energy balance. The skill of the JULES (Joint UK Land Environment Simulator) land surface model (LSM) to simulate key soil variables, such as soil moisture content and surface temperature, and fluxes such as evaporation, is investigated. The Richards equation for soil water transfer, as used in most LSMs, was updated by incorporating isothermal and thermal water vapour transfer. The model was tested for three sites representative of semi-arid and temperate arid climates: the Jornada site (New Mexico, USA), Griffith site (Australia) and Audubon site (Arizona, USA). Water vapour flux was found to contribute significantly to the water and heat transfer in the upper soil layers. This was mainly due to isothermal vapour diffusion; thermal vapour flux also played a role at the Jornada site just after rainfall events. Inclusion of water vapour flux had an effect on the diurnal evolution of evaporation, soil moisture content and surface temperature. The incorporation of additional processes, such as water vapour flux among others, into LSMs may improve the coupling between the upper soil layers and the atmosphere, which in turn could increase the reliability of weather and climate predictions.

Key concepts in development geography

Key Concepts in Develpment Geography is an introductory text that provides students with the core concepts that form contemporary research and ideas within the development geography discipline. The book covers the meanings and measurement of development; its theory and practice; work, employment and development; people, culture and development; and contemporary issues in development.

Managing the duality of IHRM: unravelling the strategy and perceptions of key actors in South Korean MNCs

This research aims to extend our understanding of the duality between global integration and local responsiveness in multinational corporations (MNCs) by exploring the perceptions of corporate HR actors regarding the intra-organisational factors that alter the balance between these pressures. It examines the perceptions and actions of key actors in the context of two Korean MNCs. The study shows the importance attributed to a range of socio-procedural factors by corporate actors and which, therefore, inform the practical management of the dual forces, notably: HR expertise, social ties, trustworthy relationships and co-involvement in decision processes.

Domestic cold pitched roofs in the UK: - effect of using different roof insulation materials

The type and thickness of insulation on the topside horizontal of cold pitched roofs has a significant role in controlling air movement, energy conservation and moisture transfer reduction through the ceiling to the loft (roof void) space. To investigate its importance, a numerical model using a HAM software package on a Matlab platform with a Simulink simulation tool has been developed using insitu measurements of airflows from the dwelling space through the ceiling to the loft of three houses of different configurations and loft space. Considering typical UK roof underlay (i.e. bituminous felt and a vapour permeable underlay), insitu measurements of the 3 houses were tested using a calibrated passive sampling technique. Using the measured airflows, the effect of air movement on three types of roof insulation (i.e. fibreglass, cellulose and foam) was modelled to investigate associated energy losses and moisture transport. The thickness of the insulation materials were varied but the ceiling airtightness and eaves gap size were kept constant. These instances were considered in order to visualize the effects of the changing parameters. In addition, two different roof underlays of varying resistances were considered and compared to access the influence of the underlay, if any, on energy conservation. The comparison of these insulation materials in relation to the other parameters showed that the type of insulation material and thickness, contributes significantly to energy conservation and moisture transfer reduction through the roof and hence of the building as a whole.

PMCRI: a parallel modular classification rule induction framework

In a world where massive amounts of data are recorded on a large scale we need data mining technologies to gain knowledge from the data in a reasonable time. The Top Down Induction of Decision Trees (TDIDT) algorithm is a very widely used technology to predict the classification of newly recorded data. However alternative technologies have been derived that often produce better rules but do not scale well on large datasets. Such an alternative to TDIDT is the PrismTCS algorithm. PrismTCS performs particularly well on noisy data but does not scale well on large datasets. In this paper we introduce Prism and investigate its scaling behaviour. We describe how we improved the scalability of the serial version of Prism and investigate its limitations. We then describe our work to overcome these limitations by developing a framework to parallelise algorithms of the Prism family and similar algorithms. We also present the scale up results of a first prototype implementation.

Parallel induction of modular classification rules

The Distributed Rule Induction (DRI) project at the University of Portsmouth is concerned with distributed data mining algorithms for automatically generating rules of all kinds. In this paper we present a system architecture and its implementation for inducing modular classification rules in parallel in a local area network using a distributed blackboard system. We present initial results of a prototype implementation based on the Prism algorithm.

Parallel rule induction with information theoretic pre-pruning

In a world where data is captured on a large scale the major challenge for data mining algorithms is to be able to scale up to large datasets. There are two main approaches to inducing classification rules, one is the divide and conquer approach, also known as the top down induction of decision trees; the other approach is called the separate and conquer approach. A considerable amount of work has been done on scaling up the divide and conquer approach. However, very little work has been conducted on scaling up the separate and conquer approach.In this work we describe a parallel framework that allows the parallelisation of a certain family of separate and conquer algorithms, the Prism family. Parallelisation helps the Prism family of algorithms to harvest additional computer resources in a network of computers in order to make the induction of classification rules scale better on large datasets. Our framework also incorporates a pre-pruning facility for parallel Prism algorithms.

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.

Parallel random prism: a computationally efficient ensemble learner for classification

Generally classifiers tend to overfit if there is noise in the training data or there are missing values. Ensemble learning methods are often used to improve a classifier's classification accuracy. Most ensemble learning approaches aim to improve the classification accuracy of decision trees. However, alternative classifiers to decision trees exist. The recently developed Random Prism ensemble learner for classification aims to improve an alternative classification rule induction approach, the Prism family of algorithms, which addresses some of the limitations of decision trees. However, Random Prism suffers like any ensemble learner from a high computational overhead due to replication of the data and the induction of multiple base classifiers. Hence even modest sized datasets may impose a computational challenge to ensemble learners such as Random Prism. Parallelism is often used to scale up algorithms to deal with large datasets. This paper investigates parallelisation for Random Prism, implements a prototype and evaluates it empirically using a Hadoop computing cluster.

Carbon monoxide inhibits L-type Ca2+ channels via redox modulation of key cysteine residues by mitochondrial reactive oxygen species

Conditions of stress, such as myocardial infarction, stimulate up-regulation of heme oxygenase (HO-1) to provide cardioprotection. Here, we show that CO, a product of heme catabolism by HO-1, directly inhibits native rat cardiomyocyte L-type Ca2+ currents and the recombinant alpha1C subunit of the human cardiac L-type Ca2+ channel. CO (applied via a recognized CO donor molecule or as the dissolved gas) caused reversible, voltage-independent channel inhibition, which was dependent on the presence of a spliced insert in the cytoplasmic C-terminal region of the channel. Sequential molecular dissection and point mutagenesis identified three key cysteine residues within the proximal 31 amino acids of the splice insert required for CO sensitivity. CO-mediated inhibition was independent of nitric oxide and protein kinase G but was prevented by antioxidants and the reducing agent, dithiothreitol. Inhibition of NADPH oxidase and xanthine oxidase did not affect the inhibitory actions of CO. Instead, inhibitors of complex III (but not complex I) of the mitochondrial electron transport chain and a mitochondrially targeted antioxidant (Mito Q) fully prevented the effects of CO. Our data indicate that the cardioprotective effects of HO-1 activity may be attributable to an inhibitory action of CO on cardiac L-type Ca2+ channels. Inhibition arises from the ability of CO to promote generation of reactive oxygen species from complex III of mitochondria. This in turn leads to redox modulation of any or all of three critical cysteine residues in the channel's cytoplasmic C-terminal tail, resulting in channel inhibition.

Establishing a reliable Jini infrastructure for parallel applications

Java is becoming an increasingly popular language for developing distributed and parallel scientific and engineering applications. Jini is a Java-based infrastructure developed by Sun that can allegedly provide all the services necessary to support distributed applications. It is the aim of this paper to explore and investigate the services and properties that Jini actually provides and match these against the needs of high performance distributed and parallel applications written in Java. The motivation for this work is the need to develop a distributed infrastructure to support an MPI-like interface to Java known as MPJ. In the first part of the paper we discuss the needs of MPJ, the parallel environment that we wish to support. In particular we look at aspects such as reliability and ease of use. We then move on to sketch out the Jini architecture and review the components and services that Jini provides. In the third part of the paper we critically explore a Jini infrastructure that could be used to support MPJ. Here we are particularly concerned with Jini's ability to support reliably a cocoon of MPJ processes executing in a heterogeneous envirnoment. In the final part of the paper we summarise our findings and report on future work being undertaken on Jini and MPJ.

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.

Trust and relational embeddedness: exploring a paradox of trust pattern development in key supplier relationships

This paper explores the role of trust as an enabler and constraint between buyers and suppliers engaged in long-term relationships. According to the relational view, cooperative strategies require trust-based mutual commitments to co-create value. However, complete pictures of the positive and negative outcomes from trust development have yet to be fully developed. In particular, trust as an originator of path dependent constraints resulting from over embeddedness is yet to be integrated into the relational view. We use a case-based methodology to explore whether trust is an optimizing phenomenon in key supplier relationships. Two cases where trust development processes demonstrate a paradox of trust-building behaviors cultivate different outcomes constraining value co-creation.