Individual assets, market structure and the drivers of return

Much prior research on the structure and performance of UK real estate portfolios has relied on aggregated measures for sector and region. For these groupings to have validity, the performance of individual properties within each group should be similar. This paper analyses a sample of 1,200 properties using multiple discriminant analysis and cluster analysis techniques. It is shown that conventional property type and spatial classifications do not capture the variation in return behaviour at the individual building level. The major feature is heterogeneity - but there may be distinctions between growth and income properties and between single and multi-let properties that could help refine portfolio structures.

Gene-chip technology and its applications

Gene Chips are finding extensive use in animal and plant science. Generally microarrays are of two kind, cDNA or oligonucleotide. cDNA microarrays were developed at Stanford University, whereas oligonucleotide were developed by Affymetrix. The construction of cDNA or oligonucleotide on a glass slide helps to compare the gene expression level of treated and control samples by labeling mRNA with green (Cy3) and red (Cy5) dyes. The hybridized gene chip emit fluorescence whose intensity and colour can be measured. RNA labeling can be done directly or indirectly. Indirect method involves amino allyle modified dUTP instead of pre-labelled nucleotide. Hybridization of gene chip generally occurs in a minimum volume possible and to ensure the hetroduplex formation, a ten fold more DNA is spotted on slide than in the solutions. A confocal or semi confocal laser technologies coupled with CCD camera are used for image acquisition. For standardization, house keeping genes are used or cDNA are spotted in gene chip that are not present in treated or control samples. Moreover, statistical analysis (image analysis) and cluster analysis softwares have been developed by Stanford University. The gene-chip technology has many applications like expression analysis, gene expression signatures (molecular phenotypes) and promoter regulatory element co-expression.

Re-thinking commercial real estate market segmentation

Investments in direct real estate are inherently difficult to segment compared to other asset classes due to the complex and heterogeneous nature of the asset. The most common segmentation in real estate investment analysis relies on property sector and geographical region. In this paper, we compare the predictive power of existing industry classifications with a new type of segmentation using cluster analysis on a number of relevant property attributes including the equivalent yield and size of the property as well as information on lease terms, number of tenants and tenant concentration. The new segments are shown to be distinct and relatively stable over time. In a second stage of the analysis, we test whether the newly generated segments are able to better predict the resulting financial performance of the assets than the old dichotomous segments. Applying both discriminant and neural network analysis we find mixed evidence for this hypothesis. Overall, we conclude from our analysis that each of the two approaches to segmenting the market has its strengths and weaknesses so that both might be applied gainfully in real estate investment analysis and fund management.

A typology of internet users based on comparative affective states: evidence from eight countries

Purpose - The role of affective states in consumer behaviour is well established. However, no study to date has empirically examined online affective states as a basis for constructing typologies of internet users and for assessing the invariance of clusters across national cultures. Design/methodology/approach - Four focus groups with internet users were carried out to adapt a set of affective states identified from the literature to the online environment. An online survey was then designed to collect data from internet users in four Western and four East Asian countries. Findings - Based on a cluster analysis, six cross-national market segments are identified and labelled "Positive Online Affectivists", "Offline Affectivists", "On/Off-line Negative Affectivists", "Online Affectivists", "Indistinguishable Affectivists", and "Negative Offline Affectivists". The resulting clusters discriminate on the basis of national culture, gender, working status and perceptions towards online brands. Practical implications - Marketers may use this typology to segment internet users in order to predict their perceptions towards online brands. Also, a standardised approach to e-marketing is not recommended on the basis of affective state-based segmentation. Originality/value - This is the first study proposing affective state-based typologies of internet users using comparable samples from four Western and four East Asian countries.

Proanthocyanidin diversity in the EU 'HealthyHay' sainfoin (onobrychis viciifolia) germplasm collection

This study investigated 37 diverse sainfoin (Onobrychis viciifolia Scop.) accessions from the EU ‘HealthyHay’ germplasm collection for proanthocyanidin (PA) content and composition. Accessions displayed a wide range of differences: PA contents varied from 0.57 to 2.80 g/100 g sainfoin; the mean degree of polymerisation from 12 to 84; the proportion of prodelphinidin tannins from 53% to 95%, and the proportion of trans-flavanol units from 12% to 34%. A positive correlation was found between PA contents (thiolytic versus acid–butanol degradation; P < 0.001; R2 = 0.49). A negative correlation existed between PA content (thiolysis) and mDP (P < 0.05; R2 = −0.30), which suggested that accessions with high PA contents had smaller PA polymers. Cluster analysis revealed that European accessions clustered into two main groups: Western Europe and Eastern Europe/Asia. In addition, accessions from USA, Canada and Armenia tended to cluster together. Overall, there was broad agreement between tannin clusters and clusters that were based on morphological and agronomic characteristics.

Farm and socio-economic characteristics of smallholder milk producers and their influence on technology adoption in Central Mexico

In order to identify the factors influencing adoption of technologies promoted by government to small-scale dairy farmers in the highlands of central Mexico, a field survey was conducted. A total of 115 farmers were grouped through cluster analysis (CA) and divided into three wealth status categories (high, medium and low) using wealth ranking. Chi-square analysis was used to examine the association of wealth status with technology adoption. Four groups of farms were differentiated in terms of farms’ dimensions, farmers’ education, sources of incomes, wealth status, management of herd, monetary support by government and technological availability. Statistical differences (p < 0.05) were observed in the milk yield per herd per year among groups. Government organizations (GO) participated little in the promotion of the 17 technologies identified, six of which focused on crop or forage production and 11 of which were related to animal husbandry. Relatives and other farmers played an important role in knowledge diffusion and technology adoption. Although wealth status had a significant association (p < 0.05) with adoption, other factors including importance of the technology to farmers, usefulness and productive benefits of innovations together with farmers’ knowledge of them, were important. It is concluded that the analysis of the information per group and wealth status was useful to identify suitable crop or forage related and animal husbandry technologies per group and wealth status of farmers. Therefore the characterizations of farmers could provide a useful starting point for the design and delivery of more appropriate and effective extension.

Epidemic K-Means clustering

The K-Means algorithm for cluster analysis is one of the most influential and popular data mining methods. Its straightforward parallel formulation is well suited for distributed memory systems with reliable interconnection networks. However, in large-scale geographically distributed systems the straightforward parallel algorithm can be rendered useless by a single communication failure or high latency in communication paths. This work proposes a fully decentralised algorithm (Epidemic K-Means) which does not require global communication and is intrinsically fault tolerant. The proposed distributed K-Means algorithm provides a clustering solution which can approximate the solution of an ideal centralised algorithm over the aggregated data as closely as desired. A comparative performance analysis is carried out against the state of the art distributed K-Means algorithms based on sampling methods. The experimental analysis confirms that the proposed algorithm is a practical and accurate distributed K-Means implementation for networked systems of very large and extreme scale.

Fault tolerant decentralised K-Means clustering for asynchronous large-scale networks

The K-Means algorithm for cluster analysis is one of the most influential and popular data mining methods. Its straightforward parallel formulation is well suited for distributed memory systems with reliable interconnection networks, such as massively parallel processors and clusters of workstations. However, in large-scale geographically distributed systems the straightforward parallel algorithm can be rendered useless by a single communication failure or high latency in communication paths. The lack of scalable and fault tolerant global communication and synchronisation methods in large-scale systems has hindered the adoption of the K-Means algorithm for applications in large networked systems such as wireless sensor networks, peer-to-peer systems and mobile ad hoc networks. This work proposes a fully distributed K-Means algorithm (EpidemicK-Means) which does not require global communication and is intrinsically fault tolerant. The proposed distributed K-Means algorithm provides a clustering solution which can approximate the solution of an ideal centralised algorithm over the aggregated data as closely as desired. A comparative performance analysis is carried out against the state of the art sampling methods and shows that the proposed method overcomes the limitations of the sampling-based approaches for skewed clusters distributions. The experimental analysis confirms that the proposed algorithm is very accurate and fault tolerant under unreliable network conditions (message loss and node failures) and is suitable for asynchronous networks of very large and extreme scale.

An overview of the use of neural networks for data mining tasks

In the recent years, the area of data mining has been experiencing considerable demand for technologies that extract knowledge from large and complex data sources. There has been substantial commercial interest as well as active research in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from large datasets. Artificial neural networks (NNs) are popular biologically-inspired intelligent methodologies, whose classification, prediction, and pattern recognition capabilities have been utilized successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction, and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks. © 2012 Wiley Periodicals, Inc.

Non-uniform data distribution for communication-efficient parallel clustering

Global communicationrequirements andloadimbalanceof someparalleldataminingalgorithms arethe major obstacles to exploitthe computational power of large-scale systems. This work investigates how non-uniform data distributions can be exploited to remove the global communication requirement and to reduce the communication costin parallel data mining algorithms and, in particular, in the k-means algorithm for cluster analysis. In the straightforward parallel formulation of the k-means algorithm, data and computation loads are uniformly distributed over the processing nodes. This approach has excellent load balancing characteristics that may suggest it could scale up to large and extreme-scale parallel computing systems. However, at each iteration step the algorithm requires a global reduction operationwhichhinders thescalabilityoftheapproach.Thisworkstudiesadifferentparallelformulation of the algorithm where the requirement of global communication is removed, while maintaining the same deterministic nature ofthe centralised algorithm. The proposed approach exploits a non-uniform data distribution which can be either found in real-world distributed applications or can be induced by means ofmulti-dimensional binary searchtrees. The approachcanalso be extended to accommodate an approximation error which allows a further reduction ofthe communication costs. The effectiveness of the exact and approximate methods has been tested in a parallel computing system with 64 processors and in simulations with 1024 processing element

Classification of cortical microcircuits based on micro-electrode-array data from slices of rat barrel cortex

The bewildering complexity of cortical microcircuits at the single cell level gives rise to surprisingly robust emergent activity patterns at the level of laminar and columnar local field potentials (LFPs) in response to targeted local stimuli. Here we report the results of our multivariate data-analytic approach based on simultaneous multi-site recordings using micro-electrode-array chips for investigation of the microcircuitary of rat somatosensory (barrel) cortex. We find high repeatability of stimulus-induced responses, and typical spatial distributions of LFP responses to stimuli in supragranular, granular, and infragranular layers, where the last form a particularly distinct class. Population spikes appear to travel with about 33 cm/s from granular to infragranular layers. Responses within barrel related columns have different profiles than those in neighbouring columns to the left or interchangeably to the right. Variations between slices occur, but can be minimized by strictly obeying controlled experimental protocols. Cluster analysis on normalized recordings indicates specific spatial distributions of time series reflecting the location of sources and sinks independent of the stimulus layer. Although the precise correspondences between single cell activity and LFPs are still far from clear, a sophisticated neuroinformatics approach in combination with multi-site LFP recordings in the standardized slice preparation is suitable for comparing normal conditions to genetically or pharmacologically altered situations based on real cortical microcircuitry.

Dynamic group communication for large-scale parallel data mining

Exascale systems are the next frontier in high-performance computing and are expected to deliver a performance of the order of 10^18 operations per second using massive multicore processors. Very large- and extreme-scale parallel systems pose critical algorithmic challenges, especially related to concurrency, locality and the need to avoid global communication patterns. This work investigates a novel protocol for dynamic group communication that can be used to remove the global communication requirement and to reduce the communication cost in parallel formulations of iterative data mining algorithms. The protocol is used to provide a communication-efficient parallel formulation of the k-means algorithm for cluster analysis. The approach is based on a collective communication operation for dynamic groups of processes and exploits non-uniform data distributions. Non-uniform data distributions can be either found in real-world distributed applications or induced by means of multidimensional binary search trees. The analysis of the proposed dynamic group communication protocol has shown that it does not introduce significant communication overhead. The parallel clustering algorithm has also been extended to accommodate an approximation error, which allows a further reduction of the communication costs. The effectiveness of the exact and approximate methods has been tested in a parallel computing system with 64 processors and in simulations with 1024 processing elements.

Efficient group communication for large-scale parallel clustering

Global communication requirements and load imbalance of some parallel data mining algorithms are the major obstacles to exploit the computational power of large-scale systems. This work investigates how non-uniform data distributions can be exploited to remove the global communication requirement and to reduce the communication cost in iterative parallel data mining algorithms. In particular, the analysis focuses on one of the most influential and popular data mining methods, the k-means algorithm for cluster analysis. The straightforward parallel formulation of the k-means algorithm requires a global reduction operation at each iteration step, which hinders its scalability. This work studies a different parallel formulation of the algorithm where the requirement of global communication can be relaxed while still providing the exact solution of the centralised k-means algorithm. The proposed approach exploits a non-uniform data distribution which can be either found in real world distributed applications or can be induced by means of multi-dimensional binary search trees. The approach can also be extended to accommodate an approximation error which allows a further reduction of the communication costs.

Can perceptual differences account for managerial success?: the case of Japanese aid workers

Purpose – There is a wealth of studies which suggest that managers' positive perceptions/expectations can considerably influence the organisational performance; unfortunately, little empirical evidence has been obtained from development studies. This research aims to focus on the perceptual and behavioural trait differences of successful and unsuccessful aid workers, and their relationship with organisational performance. Design/methodology/approach – Through web-based survey, 244 valid responses were obtained from the Japan International Cooperation Agency (JICA)-aid managers worldwide. Five perception related factors were extracted and used for cluster analysis to group the respondents. Each cluster's perception/behaviour-related factors and organisational performance variables were compared by ANOVA. Findings – It was discovered that Japanese's positive perception/expectation about work and their local colleagues was related to higher organisational performance, and conversely, the negative perception on their part was generally associated with negative behaviour and lower organisational performance. Moreover, in a development context, lower work-related stress and feelings of resignation toward work were strongly associated with the acceptability of cross-cultural work environment. Practical implications – The differences in perceptual tendencies suggest that cautious consideration is advised since these findings may mainly apply to Japanese aid managers. However, as human nature is universal, positive perception and behaviour would bring out positive output in most organisations. Originality/value – This study extended the contextualised “Pygmalion effect” and has clarified the influence of perception/expectation on counter-part behaviour and organisational performance in development aid context, where people-related issues have often been ignored. This first-time research provides imperial data on the significant role of positive perception on the incumbent role holder.

Developing green supply chain management taxonomy-based decision support system

The aim of this paper is to develop a comprehensive taxonomy of green supply chain management (GSCM) practices and develop a structural equation modelling-driven decision support system following GSCM taxonomy for managers to provide better understanding of the complex relationship between the external and internal factors and GSCM operational practices. Typology and/or taxonomy play a key role in the development of social science theories. The current taxonomies focus on a single or limited component of the supply chain. Furthermore, they have not been tested using different sample compositions and contexts, yet replication is a prerequisite for developing robust concepts and theories. In this paper, we empirically replicate one such taxonomy extending the original study by (a) developing broad (containing the key components of supply chain) taxonomy; (b) broadening the sample by including a wider range of sectors and organisational size; and (c) broadening the geographic scope of the previous studies. Moreover, we include both objective measures and subjective attitudinal measurements. We use a robust two-stage cluster analysis to develop our GSCM taxonomy. The main finding validates the taxonomy previously proposed and identifies size, attitude and level of environmental risk and impact as key mediators between internal drivers, external drivers and GSCM operational practices.