Reliability of vegetation community information derived using decorana ordination and fuzzy c-means clustering

Descriptions of vegetation communities are often based on vague semantic terms describing species presence and dominance. For this reason, some researchers advocate the use of fuzzy sets in the statistical classification of plant species data into communities. In this study, spatially referenced vegetation abundance values collected from Greek phrygana were analysed by ordination (DECORANA), and classified on the resulting axes using fuzzy c-means to yield a point data-set representing local memberships in characteristic plant communities. The fuzzy clusters matched vegetation communities noted in the field, which tended to grade into one another, rather than occupying discrete patches. The fuzzy set representation of the community exploited the strengths of detrended correspondence analysis while retaining richer information than a TWINSPAN classification of the same data. Thus, in the absence of phytosociological benchmarks, meaningful and manageable habitat information could be derived from complex, multivariate species data. We also analysed the influence of the reliability of different surveyors' field observations by multiple sampling at a selected sample location. We show that the impact of surveyor error was more severe in the Boolean than the fuzzy classification. © 2007 Springer.

Fuzzy c-means variants for medical image segmentation

Segmentation is an important step in many medical imaging applications and a variety of image segmentation techniques exist. One group of segmentation algorithms is based on clustering concepts. In this article we investigate several fuzzy c-means based clustering algorithms and their application to medical image segmentation. In particular we evaluate the conventional hard c-means (HCM) and fuzzy c-means (FCM) approaches as well as three computationally more efficient derivatives of fuzzy c-means: fast FCM with random sampling, fast generalised FCM, and a new anisotropic mean shift based FCM. © 2010 by IJTS, ISDER.

What to do when K-means clustering fails:a simple yet principled alternative algorithm

The K-means algorithm is one of the most popular clustering algorithms in current use as it is relatively fast yet simple to understand and deploy in practice. Nevertheless, its use entails certain restrictive assumptions about the data, the negative consequences of which are not always immediately apparent, as we demonstrate. While more flexible algorithms have been developed, their widespread use has been hindered by their computational and technical complexity. Motivated by these considerations, we present a flexible alternative to K-means that relaxes most of the assumptions, whilst remaining almost as fast and simple. This novel algorithm which we call MAP-DP (maximum a-posteriori Dirichlet process mixtures), is statistically rigorous as it is based on nonparametric Bayesian Dirichlet process mixture modeling. This approach allows us to overcome most of the limitations imposed by K-means. The number of clusters K is estimated from the data instead of being fixed a-priori as in K-means. In addition, while K-means is restricted to continuous data, the MAP-DP framework can be applied to many kinds of data, for example, binary, count or ordinal data. Also, it can efficiently separate outliers from the data. This additional flexibility does not incur a significant computational overhead compared to K-means with MAP-DP convergence typically achieved in the order of seconds for many practical problems. Finally, in contrast to K-means, since the algorithm is based on an underlying statistical model, the MAP-DP framework can deal with missing data and enables model testing such as cross validation in a principled way. We demonstrate the simplicity and effectiveness of this algorithm on the health informatics problem of clinical sub-typing in a cluster of diseases known as parkinsonism.

Exploring ant-based algorithms for gene expression data analysis

Objective: Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant colony optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper investigates ant-based algorithms for gene expression data clustering and associative classification. Methods and material: An ant-based clustering (Ant-C) and an ant-based association rule mining (Ant-ARM) algorithms are proposed for gene expression data analysis. The proposed algorithms make use of the natural behavior of ants such as cooperation and adaptation to allow for a flexible robust search for a good candidate solution. Results: Ant-C has been tested on the three datasets selected from the Stanford Genomic Resource Database and achieved relatively high accuracy compared to other classical clustering methods. Ant-ARM has been tested on the acute lymphoblastic leukemia (ALL)/acute myeloid leukemia (AML) dataset and generated about 30 classification rules with high accuracy. Conclusions: Ant-C can generate optimal number of clusters without incorporating any other algorithms such as K-means or agglomerative hierarchical clustering. For associative classification, while a few of the well-known algorithms such as Apriori, FP-growth and Magnum Opus are unable to mine any association rules from the ALL/AML dataset within a reasonable period of time, Ant-ARM is able to extract associative classification rules.

The classification of HLA supertypes by GRID/CPCA and hierarchical clustering methods

Biological experiments often produce enormous amount of data, which are usually analyzed by data clustering. Cluster analysis refers to statistical methods that are used to assign data with similar properties into several smaller, more meaningful groups. Two commonly used clustering techniques are introduced in the following section: principal component analysis (PCA) and hierarchical clustering. PCA calculates the variance between variables and groups them into a few uncorrelated groups or principal components (PCs) that are orthogonal to each other. Hierarchical clustering is carried out by separating data into many clusters and merging similar clusters together. Here, we use an example of human leukocyte antigen (HLA) supertype classification to demonstrate the usage of the two methods. Two programs, Generating Optimal Linear Partial Least Square Estimations (GOLPE) and Sybyl, are used for PCA and hierarchical clustering, respectively. However, the reader should bear in mind that the methods have been incorporated into other software as well, such as SIMCA, statistiXL, and R.

The separation of secondary oil-water dispersions in particulate beds

The literature relating to haze formation, methods of separation, coalescence mechanisms, and models by which droplets <100 μm are collected, coalesced and transferred, have been reviewed with particular reference to particulate bed coalescers. The separation of secondary oil-water dispersions was studied experimentally using packed beds of monosized glass ballotini particles. The variables investigated were superficial velocity, bed depth, particle size, and the phase ratio and drop size distribution of inlet secondary dispersion. A modified pump loop was used to generate secondary dispersions of toluene or Clairsol 350 in water with phase ratios between 0.5-6.0 v/v%.Inlet drop size distributions were determined using a Malvern Particle Size Analyser;effluent, coalesced droplets were sized by photography. Single phase flow pressure drop data were correlated by means of a Carman-Kozeny type equation. Correlations were obtained relating single and two phase pressure drops, as (ΔP2/μc)/ΔP1/μd) = kp Ua Lb dcc dpd Cine A flow equation was derived to correlate the two phase pressure drop data as, ΔP2/(ρcU2) = 8.64*107 [dc/D]-0.27 [L/D]0.71 [dp/D]-0.17 [NRe]1.5 [e1]-0.14 [Cin]0.26  In a comparison between functions to characterise the inlet drop size distributions a modification of the Weibull function provided the best fit of experimental data. The general mean drop diameter was correlated by: q_p q_p p_q /β      Γ ((q-3/β) +1) d qp = d fr  .α        Γ ((P-3/β +1 The measured and predicted mean inlet drop diameters agreed within ±15%. Secondary dispersion separation depends largely upon drop capture within a bed. A theoretical analysis of drop capture mechanisms in this work indicated that indirect interception and London-van der Waal's mechanisms predominate. Mathematical models of dispersed phase concentration m the bed were developed by considering drop motion to be analogous to molecular diffusion.The number of possible channels in a bed was predicted from a model in which the pores comprised randomly-interconnected passage-ways between adjacent packing elements and axial flow occured in cylinders on an equilateral triangular pitch. An expression was derived for length of service channels in a queuing system leading to the prediction of filter coefficients. The insight provided into the mechanisms of drop collection and travel, and the correlations of operating parameters, should assist design of industrial particulate bed coalescers.

Phosphatidylserine-containing liposomes:modulators of rhinovirus induced inflammatory responses

Background: Human rhinoviral infections are major contributors to the healthcare burden associated with acute exacerbations of asthma. We, and others have recently demonstrated that rhinovirus (RV)-induced inflammatory responses are mediated by multiple signalling mechanisms, such as IL-1/MyD88 (1) and TLR3/RIGI (2). We have also previously published work showing that TLR signalling is effectively inhibited by phosphatidylserine-containing liposomes (SAPS), through the disruption of membrane microdomains (3). Evidence has also suggested that membrane microdomains may influence infections with RV. In this study, we explored the ability of SAPS to modulate responses to the natural viral pathogens, RV-1B and RV-16. Method: The immortalized bronchial epithelial cell line, BEAS-2B or primary bronchial epithelial cells were infected with RV-1B or RV-16 at a TCID50/ml of 19107 for 1 h. Immediately following infection, various concentrations of SAPS were added and changes in cytokine release were measured at 24 h. SAPS remained present throughout. Type I and III interferon (IFN) expression and rates of viral replication were measured by quantitative PCR. Virus quantification was also performed using a viral CPE assay, and IFN signalling was measured by western blot. Liposome stability was characterised and intracellular trafficking of fluorescently labelled SAPS in BEAS-2B cells was investigated using confocal microscopy. For in vivo studies, female wt Balb/c mice were pre-treated with SAPS for 2 h prior to infection with RV as previously described and changes in BAL cell number, BAL cytokine production and viral replication were quantified (4). Results: Characterisation of SAPS liposomes by mass spectrometry showed no obvious signs of oxidation over the time period tested, and liposome size remained constant. Preliminary confocal studies revealed that SAPS was rapidly internalised within the cell and was found to associate with intracellular compartments such as the early endosome and golgi. Viral infected BEAS-2B cells co-incubated with SAPS, showed notably impaired responses to RV as assessed by release of CXCL8 and CCL5. SAPS also reduced RV-induced IFNb production and STAT-1 phosphorylation, without significantly influencing viral replication rates. Modest increases in viral particle production were only observed at 48 and 72 h time points. Suppression of viral-induced cytokine production was also observed in primary bronchial epithelial cells and pilot in vivo studies showed that SAPS results in reduced KC production at 24 h post viral infection, and this was associated with reduced neutrophil numbers within the BAL fluid. Conclusion: Our data demonstrates a potential means of modulating inflammatory responses induced by human rhinovirus.

Development of a technological innovation capability assessment model:a case study of manufacturing SMEs in Sialkot, Pakistan

Small and Medium Enterprises (SMEs) play an important part in the economy of any country. Initially, a flat management hierarchy, quick response to market changes and cost competitiveness were seen as the competitive characteristics of an SME. Recently, in developed economies, technological capabilities (TCs) management- managing existing and developing or assimilating new technological capabilities for continuous process and product innovations, has become important for both large organisations and SMEs to achieve sustained competitiveness. Therefore, various technological innovation capability (TIC) models have been developed at firm level to assess firms‘ innovation capability level. These models output help policy makers and firm managers to devise policies for deepening a firm‘s technical knowledge generation, acquisition and exploitation capabilities for sustained technological competitive edge. However, in developing countries TCs management is more of TCs upgrading: acquisitions of TCs from abroad, and then assimilating, innovating and exploiting them. Most of the TIC models for developing countries delineate the level of TIC required as firms move from the acquisition to innovative level. However, these models do not provide tools for assessing the existing level of TIC of a firm and various factors affecting TIC, to help practical interventions for TCs upgrading of firms for improved or new processes and products. Recently, the Government of Pakistan (GOP) has realised the importance of TCs upgrading in SMEs-especially export-oriented, for their sustained competitiveness. The GOP has launched various initiatives with local and foreign assistance to identify ways and means of upgrading local SMEs capabilities. This research targets this gap and developed a TICs assessment model for identifying the existing level of TIC of manufacturing SMEs existing in clusters in Sialkot, Pakistan. SME executives in three different export-oriented clusters at Sialkot were interviewed to analyse technological capabilities development initiatives (CDIs) taken by them to develop and upgrade their firms‘ TCs. Data analysed at CDI, firm, cluster and cross-cluster level first helped classify interviewed firms as leader, follower and reactor, with leader firms claiming to introduce mostly new CDIs to their cluster. Second, the data analysis displayed that mostly interviewed leader firms exhibited ‗learning by interacting‘ and ‗learning by training‘ capabilities for expertise acquisition from customers and international consultants. However, these leader firms did not show much evidence of learning by using, reverse engineering and R&D capabilities, which according to the extant literature are necessary for upgrading existing TIC level and thus TCs of firm for better value-added processes and products. The research results are supported by extant literature on Sialkot clusters. Thus, in sum, a TIC assessment model was developed in this research which qualitatively identified interviewed firms‘ TIC levels, the factors affecting them, and is validated by existing literature on interviewed Sialkot clusters. Further, the research gives policy level recommendations for TIC and thus TCs upgrading at firm and cluster level for targeting better value-added markets.

Fatty acid derivatised analogues of glucose-dependent insulinotropic polypeptide with improved antihyperglycaemic and insulinotropic properties

C-terminal acylation of Lys(37) with myristic (MYR; tetradecanoic acid), palmitic (PAL; hexadecanoic acid) and stearic (octadecanoic acid) fatty acids with or without N-terminal acetylation was employed to develop long-acting analogues of the glucoregulatory hormone, glucose-dependent insulinotropic polypeptide (GIP). All GIP analogues exhibited resistance to dipeptidylpeptidase-IV (DPP-IV) and significantly improved in vitro cAMP production and insulin secretion. Administration of GIP analogues to ob/ob mice significantly lowered plasma glucose-GIP(Lys(37)MYR), N-AcGIP(Lys(37)MYR) and GIP(Lys(37)PAL) increased plasma insulin concentrations. GIP(Lys(37)MYR) and N-AcGIP(Lys(37)MYR) elicited protracted glucose-lowering effects when administered 24h prior to an intraperitoneal glucose load. Daily administration of GIP(Lys(37)MYR) and N-AcGIP(Lys(37)MYR) to ob/ob mice for 24 days decreased glucose and significantly improved plasma insulin, glucose tolerance and beta-cell glucose responsiveness. Insulin sensitivity, pancreatic insulin content and triglyceride levels were not changed. These data demonstrate that C-terminal acylation particularly with myristic acid provides a class of stable, longer-acting forms of GIP for further evaluation in diabetes therapy.

Italian validation of the team climate inventory:a measure of team climate for innovation

Innovation has long been an area of interest to social scientists, and particularly to psychologists working in organisational settings. The team climate inventory (TCI) is a facet-specific measure of team climate for innovation that provides a picture of the level and quality of teamwork in a unit using a series of Likert scales. This paper describes its Italian validation in 585 working group members employed in health-related and other contexts. The data were evaluated by means of factorial analysis (including an analysis of the internal consistency of the scales) and Pearson’s product moment correlations. The results show the internal consistency of the scales and the satisfactory factorial structure of the inventory, despite some variations in the factorial structure mainly due to cultural differences and the specific nature of Italian organisational systems.

Productivity change in the water industry in England and Wales:application of the meta-Malmquist Index

This paper uses a meta-Malmquist index for measuring productivity change of the water industry in England and Wales and compares this to the traditional Malmquist index. The meta-Malmquist index computes productivity change with reference to a meta-frontier, it is computationally simpler and it is circular. The analysis covers all 22 UK water companies in existence in 2007, using data over the period 1993–2007. We focus on operating expenditure in line with assessments in this field, which treat operating and capital expenditure as lacking substitutability. We find important improvements in productivity between 1993 and 2005, most of which were due to frontier shifts rather than catch up to the frontier by companies. After 2005, the productivity shows a declining trend. We further use the meta-Malmquist index to compare the productivities of companies at the same and at different points in time. This shows some interesting results relating to the productivity of each company relative to that of other companies over time, and also how the performance of each company relative to itself over 1993–2007 has evolved. The paper is grounded in the broad theory of methods for measuring productivity change, and more specifically on the use of circular Malmquist indices for that purpose. In this context, the contribution of the paper is methodological and applied. From the methodology perspective, the paper demonstrates the use of circular meta-Malmquist indices in a comparative context not only across companies but also within company across time. This type of within-company assessment using Malmquist indices has not been applied extensively and to the authors’ knowledge not to the UK water industry. From the application perspective, the paper throws light on the performance of UK water companies and assesses the potential impact of regulation on their performance. In this context, it updates the relevant literature using more recent data.

Openness and economic growth

The themes of this thesis are that international trade and foreign direct investment (FDI) are closely related and that they have varying impacts on economic growth in countries at different stages of development. The thesis consists of three empirical studies. The first one examines the causal relationship between FDI and trade in China. The empirical study is based on a panel of bilateral data for China and 19 home countries/regions over the period 1984-98. The specific feature of the study is that econometric techniques designed specially for panel data are applied to test for unit roots and causality. The results indicate a virtuous procedure of development for China. The growth of China’s imports causes growth in inward FDI from a home country/region, which in turn causes the growth of exports from China to the home country/region. The growth of exports causes the growth of imports. This virtuous procedure is the result of China’s policy of opening to the outside world. China has been encouraging export-oriented FDI and reducing trade barriers. Such policy instruments should be further encouraged in order to enhance economic growth. In the second study, an extended gravity model is constructed to identify the main causes of recent trade growth in OECD countries. The specific features include (a) the explicit introduction of R&D and FDI as two important explanatory variables into an augmented gravity equation; (b) the adoption of a panel data approach, and (c) the careful treatment of endogeneity. The main findings are that the levels and similarities of market size, domestic R&D stock and inward FDI stock are positively related to the volume of bilateral trade, while the geographical distance, exchange rate and relative factor endowments, has a negative impact. These findings lend support to new trade, FDI and economic growth theories. The third study evaluates the impact of openness on growth in different country groups. This research distinguishes itself from many existing studies in three aspects: first, both trade and FDI are included in the measurement of openness. Second, countries are divided' into three groups according to their development stages to compare the roles of FDI and trade in different groups. Third, the possible problems of endogeneity and multicollinearity of FDI and trade are carefully dealt with in a panel data setting. The main findings are that FDI and trade are both beneficial to a country's development. However, trade has positive effects on growth in all country groups but FDI has positive effects on growth only in the country groups which have had moderate development. The findings suggest FDI and trade may affect growth under different conditions.

The geochemistry of the sedimentary rocks of the Harlech Dome, North Wales

Bedrock geochemical analysis, coupled with detailed data analysis, was carried out on some 260 samples taken from two areas of 'the Harlech Dome, near Dolgellau, North Wales. This was done to determine if rocks from mineralised and non-mineralised areas could be distinguished, and to determine mineralisation types and wall rock alterations. The Northern Area, near Talsarnau, has no recorded mineralisation, while the Southern Area, near Bontddu, has been exploited for gold. The rocks sampled, in both areas, were from the Cambrian Gamlan Flags, Clogau Shales, Vigra Flags, later vein materials, and igneous intrusions. All samples were analysed, using a new rapid, atomic absorption spectrophotometric technique, for Si, AI, Fe, Cu, Ni, Zn, Pb, Sr, Hg, and Ba. In addition 60 samples were analysed by X-ray fluorescence for Mn, Ti, Ca, K, Na, P, Cr, Ce, La, S, Y , Rh, and Th. Total CO2 was determined, on selected samples, using a combustion technique. Elemental distributions, for each rock type, in each area, were· plotted, and means, standard deviations, and enrichment indices were calculated. Multivariate statistical analysis on the results distinguished a Cu-type mineralisation in the Northern area, and both Cu and Pb/Zn types in the Southern Area. It also showed the Northern Area to be less strongly mineralised than the Southern one in which both mineralisation types are associated with wall rock alteration. Elemental associations and trends due to sedimentary processes were distinguished from those related to mineralisation. Hg is related to mineralisation, and plots of factor scores, on the sampling grid, produced clusters of mineralisation related factors in areas of known mineralisation. A double Fourier Trend Analysis program, with a wavelength search routine, was developed and used to recognise sedimentary trends for Sr. Y., Rb, and Th. These trends were interpreted to represent areas of low pH and reducing conditions. They also indicate that the supply of sediment remained constant over Gamlan, Clogau, and Vigra times. The trend surface of Hg showed no association with rock type. It is shown that analysis of a small number of samples, for a carefully selected number of elements, with detailed data analysis, can provide more useful information than analysis of a large number of samples for many elements. The mineralisation is suggested to have been the result of water solutions leaching ore metals from the sedimentary rocks and redepositing them in veins.

Differential perception of innovation with particular reference to the organisational context

Differential perception of innovation is a research area which has been advocated as a suitable topic for study in recent years. It developed from the problems encountered within earlier perception of innovation studies which sought to establish what characteristics of an innovation affected the ease of its adoption. While some success was achieved In relating perception of innovation to adoption behaviour, variability encountered Within groups expected - to fercelve innovation similarly suggested that the needs and experiences of the potential adopter were significantly affecting the research findings. Such analysis being supported by both sociological and psychological perceptual research. The present study sought to identify the presence of differential perception of innovation and explore the nature of the process. It was decided to base the research in an organisational context and to concentrate upon manufacturing innovation. It has been recognised that such adoption of technological innovation is commonly the product of a collective decision-making process, involving individuals from a variety of occupational backgrounds, both in terms of occupational speciality and level within the hierarchy. Such roles appeared likely to significantly influence perception of technological innovation, as gathered through an appropriate measure and were readily identifiable. Data vas collected by means of a face-to-face card presentation technique, a questionnaire and through case study material. Differential perception of innovation effects were apparent In the results, many similarities and differences of perception being related to the needs and experiences of the individuals studied. Phenomenological analysis, which recognises the total nature of experience in infiuencing behaviour, offered the best means of explaining the findings. It was also clear that the bureaucratic model of role definition was not applicable to the area studied, it seeming likely that such definitions are weaker under conditions of uncertainty, such as encountered in innovative decision-making.

Getting marketing back into the boardroom:the influence of the marketing department in companies today

What influence do marketing departments have in companies today? Which factors determine this influence? These are the issues discussed in the present article. Empirical evidence based on data from companies in the Netherlands demonstrates that accountability, innovativeness and customer connections are the three major drivers of influence. The need for a strong marketing department within companies is also discussed, supported by empirical data.