Use of virtual index for measuring efficiency of innovation systems : a cross-country study

In this study we propose a virtual index for measuring the relative innovativeness of countries. Using a multistage virtual benchmarking process, the best and rational benchmark is extracted for inefficient ISs. Furthermore, Tobit and Ordinary Least Squares (OLS) regression models are used to investigate the likelihood of changes in inefficiencies by investigating country-specific factors. The empirical results relating to the virtual benchmarking process suggest that the OLS regression model would better explain changes in the performance of innovation- inefficient countries.

Multiview point cloud kernels for semisupervised learning [Lecture Notes]

In semisupervised learning (SSL), a predictive model is learn from a collection of labeled data and a typically much larger collection of unlabeled data. These paper presented a framework called multi-view point cloud regularization (MVPCR), which unifies and generalizes several semisupervised kernel methods that are based on data-dependent regularization in reproducing kernel Hilbert spaces (RKHSs). Special cases of MVPCR include coregularized least squares (CoRLS), manifold regularization (MR), and graph-based SSL. An accompanying theorem shows how to reduce any MVPCR problem to standard supervised learning with a new multi-view kernel.

Time-dependent fractional advection–diffusion equations by an implicit MLS meshless method

Recently, many new applications in engineering and science are governed by a series of fractional partial differential equations (FPDEs). Unlike the normal partial differential equations (PDEs), the differential order in a FPDE is with a fractional order, which will lead to new challenges for numerical simulation, because most existing numerical simulation techniques are developed for the PDE with an integer differential order. The current dominant numerical method for FPDEs is Finite Difference Method (FDM), which is usually difficult to handle a complex problem domain, and also hard to use irregular nodal distribution. This paper aims to develop an implicit meshless approach based on the moving least squares (MLS) approximation for numerical simulation of fractional advection-diffusion equations (FADE), which is a typical FPDE. The discrete system of equations is obtained by using the MLS meshless shape functions and the meshless strong-forms. The stability and convergence related to the time discretization of this approach are then discussed and theoretically proven. Several numerical examples with different problem domains and different nodal distributions are used to validate and investigate accuracy and efficiency of the newly developed meshless formulation. It is concluded that the present meshless formulation is very effective for the modeling and simulation of the FADE.

The rademacher complexity of coregularized kernel classes

In the multi-view approach to semisupervised learning, we choose one predictor from each of multiple hypothesis classes, and we co-regularize our choices by penalizing disagreement among the predictors on the unlabeled data. We examine the co-regularization method used in the co-regularized least squares (CoRLS) algorithm, in which the views are reproducing kernel Hilbert spaces (RKHS's), and the disagreement penalty is the average squared difference in predictions. The final predictor is the pointwise average of the predictors from each view. We call the set of predictors that can result from this procedure the co-regularized hypothesis class. Our main result is a tight bound on the Rademacher complexity of the co-regularized hypothesis class in terms of the kernel matrices of each RKHS. We find that the co-regularization reduces the Rademacher complexity by an amount that depends on the distance between the two views, as measured by a data dependent metric. We then use standard techniques to bound the gap between training error and test error for the CoRLS algorithm. Experimentally, we find that the amount of reduction in complexity introduced by co regularization correlates with the amount of improvement that co-regularization gives in the CoRLS algorithm.

An advanced meshless method for time fractional diffusion equation

Recently, because of the new developments in sustainable engineering and renewable energy, which are usually governed by a series of fractional partial differential equations (FPDEs), the numerical modelling and simulation for fractional calculus are attracting more and more attention from researchers. The current dominant numerical method for modeling FPDE is Finite Difference Method (FDM), which is based on a pre-defined grid leading to inherited issues or shortcomings including difficulty in simulation of problems with the complex problem domain and in using irregularly distributed nodes. Because of its distinguished advantages, the meshless method has good potential in simulation of FPDEs. This paper aims to develop an implicit meshless collocation technique for FPDE. The discrete system of FPDEs is obtained by using the meshless shape functions and the meshless collocation formulation. The stability and convergence of this meshless approach are investigated theoretically and numerically. The numerical examples with regular and irregular nodal distributions are used to validate and investigate accuracy and efficiency of the newly developed meshless formulation. It is concluded that the present meshless formulation is very effective for the modeling and simulation of fractional partial differential equations.

Photochemistry and chemometrics : an overview

Photochemistry has made significant contributions to our understanding of many important natural processes as well as the scientific discoveries of the man-made world. The measurements from such studies are often complex and may require advanced data interpretation with the use of multivariate or chemometrics methods. In general, such methods have been applied successfully for data display, classification, multivariate curve resolution and prediction in analytical chemistry, environmental chemistry, engineering, medical research and industry. However, in photochemistry, by comparison, applications of such multivariate approaches were found to be less frequent although a variety of methods have been used, especially with spectroscopic photochemical applications. The methods include Principal Component Analysis (PCA; data display), Partial Least Squares (PLS; prediction), Artificial Neural Networks (ANN; prediction) and several models for multivariate curve resolution related to Parallel Factor Analysis (PARAFAC; decomposition of complex responses). Applications of such methods are discussed in this overview and typical examples include photodegradation of herbicides, prediction of antibiotics in human fluids (fluorescence spectroscopy), non-destructive in- and on-line monitoring (near infrared spectroscopy) and fast-time resolution of spectroscopic signals from photochemical reactions. It is also quite clear from the literature that the scope of spectroscopic photochemistry was enhanced by the application of chemometrics. To highlight and encourage further applications of chemometrics in photochemistry, several additional chemometrics approaches are discussed using data collected by the authors. The use of a PCA biplot is illustrated with an analysis of a matrix containing data on the performance of photocatalysts developed for water splitting and hydrogen production. In addition, the applications of the Multi-Criteria Decision Making (MCDM) ranking methods and Fuzzy Clustering are demonstrated with an analysis of water quality data matrix. Other examples of topics include the application of simultaneous kinetic spectroscopic methods for prediction of pesticides, and the use of response fingerprinting approach for classification of medicinal preparations. In general, the overview endeavours to emphasise the advantages of chemometrics' interpretation of multivariate photochemical data, and an Appendix of references and summaries of common and less usual chemometrics methods noted in this work, is provided. Crown Copyright © 2010.

Information sharing with key suppliers : a contingency approach

This paper focuses on information sharing with key suppliers and seeks to explore the factors that might influence its extent and depth. We also investigate how information sharing affects a company’s performance with regards to resource usage, output, and flexibility. Drawing from transaction cost- and contingency theories, several factors, namely environmental uncertainty, demand uncertainty, dependency and, the product life cycle stage are proposed to explain the level of information shared with key suppliers. We develop a model where information sharing mediates the (contingent) factors and company performance. A mail survey was used to collect data from Finnish and Swedish companies. Partial Least Squares analysis was separately performed for each country (n=119, n=102). There was consistent evidence that environmental uncertainty, demand uncertainty and supplier/buyer dependency had explanatory power, whereas no significance was found for the product life cycle stage. The results also confirm previous studies by providing support for a positive relationship between information sharing and performance, where output performance was found to be the most strongly related

Determinants and effects of information sharing with key suppliers

This paper focuses on information sharing with key suppliers and seeks to explore the factors that might influence its extent and depth. We also investigate how information sharing affects a company’s performance with regards to resource usage, output, and flexibility. Drawing from transaction cost- and contingency theories, several factors, namely environmental uncertainty, demand uncertainty, dependency and, the product life cycle stage are proposed to explain the level of information shared with key suppliers. We develop a model where information sharing mediates the (contingent) factors and company performance. A mail survey was used to collect data from Finnish and Swedish companies. Partial Least Squares analysis was separately performed for each country (n=119, n=102). There was consistent evidence that environmental uncertainty, demand uncertainty and supplier/buyer dependency had explanatory power, whereas no significance was found for the relationship between product life cycle stage and information sharing. The results also confirm previous studies by providing support for a positive relationship between information sharing and performance, where output performance was found to be the most strongly related.

Domestic and international innovation partnerships : do they matter for innovation outcomes of South African firms?

This paper studies the impact of the diversity of domestic and international innovation partnerships on the innovation outcomes of South African firms. A number of competing hypotheses are formulated and tested empirically using a sample of South African firms in manufacturing and services by applying Ordinary Least Squares regression analyses. Results show that having an innovation partnership, particularly an international partnership, is beneficial to innovation outcomes. However, it also emerges that too diverse a set of international partnerships is detrimental to innovation outcomes. The paper concludes with a discussion and a number of proposals for future research.

Simultaneous voltammetric determination of three herbicides in food and water samples with the aid of chemometrics

Differential pulse stripping voltammetry method(DPSV) was applied to the determination of three herbicides, ametryn, cyanatryn, and dimethametryn. It was found that their voltammograms overlapped strongly, and it is difficult to determine these compounds individually from their mixtures. With the aid of chemometrics, classical least squares(CLS), principal component regression(PCR) and partial least squares(PLS), voltammogram resolution and quantitative analysis of the synthetic mixtures of the three compounds were successfully performed. The proposed method was also applied to the analysis of some real samples with satisfactory results.

Validating the is-impact model in the Malaysian public sector

This research is one of several ongoing studies conducted within the IT Professional Services (ITPS) research programme at Queensland University of Technology (QUT). In 2003, ITPS introduced the IS-Impact model, a measurement model for measuring information systems success from the viewpoint of multiple stakeholders. The model, along with its instrument, is robust, simple, yet generalisable, and yields results that are comparable across time, stakeholders, different systems and system contexts. The IS-Impact model is defined as “a measure at a point in time, of the stream of net benefits from the Information System (IS), to date and anticipated, as perceived by all key-user-groups”. The model represents four dimensions, which are ‘Individual Impact’, ‘Organizational Impact’, ‘Information Quality’ and ‘System Quality’. The two Impact dimensions measure the up-to-date impact of the evaluated system, while the remaining two Quality dimensions act as proxies for probable future impacts (Gable, Sedera & Chan, 2008). To fulfil the goal of ITPS, “to develop the most widely employed model” this research re-validates and extends the IS-Impact model in a new context. This method/context-extension research aims to test the generalisability of the model by addressing known limitations of the model. One of the limitations of the model relates to the extent of external validity of the model. In order to gain wide acceptance, a model should be consistent and work well in different contexts. The IS-Impact model, however, was only validated in the Australian context, and packaged software was chosen as the IS understudy. Thus, this study is concerned with whether the model can be applied in another different context. Aiming for a robust and standardised measurement model that can be used across different contexts, this research re-validates and extends the IS-Impact model and its instrument to public sector organisations in Malaysia. The overarching research question (managerial question) of this research is “How can public sector organisations in Malaysia measure the impact of information systems systematically and effectively?” With two main objectives, the managerial question is broken down into two specific research questions. The first research question addresses the applicability (relevance) of the dimensions and measures of the IS-Impact model in the Malaysian context. Moreover, this research question addresses the completeness of the model in the new context. Initially, this research assumes that the dimensions and measures of the IS-Impact model are sufficient for the new context. However, some IS researchers suggest that the selection of measures needs to be done purposely for different contextual settings (DeLone & McLean, 1992, Rai, Lang & Welker, 2002). Thus, the first research question is as follows, “Is the IS-Impact model complete for measuring the impact of IS in Malaysian public sector organisations?” [RQ1]. The IS-Impact model is a multidimensional model that consists of four dimensions or constructs. Each dimension is represented by formative measures or indicators. Formative measures are known as composite variables because these measures make up or form the construct, or, in this case, the dimension in the IS-Impact model. These formative measures define different aspects of the dimension, thus, a measurement model of this kind needs to be tested not just on the structural relationship between the constructs but also the validity of each measure. In a previous study, the IS-Impact model was validated using formative validation techniques, as proposed in the literature (i.e., Diamantopoulos and Winklhofer, 2001, Diamantopoulos and Siguaw, 2006, Petter, Straub and Rai, 2007). However, there is potential for improving the validation testing of the model by adding more criterion or dependent variables. This includes identifying a consequence of the IS-Impact construct for the purpose of validation. Moreover, a different approach is employed in this research, whereby the validity of the model is tested using the Partial Least Squares (PLS) method, a component-based structural equation modelling (SEM) technique. Thus, the second research question addresses the construct validation of the IS-Impact model; “Is the IS-Impact model valid as a multidimensional formative construct?” [RQ2]. This study employs two rounds of surveys, each having a different and specific aim. The first is qualitative and exploratory, aiming to investigate the applicability and sufficiency of the IS-Impact dimensions and measures in the new context. This survey was conducted in a state government in Malaysia. A total of 77 valid responses were received, yielding 278 impact statements. The results from the qualitative analysis demonstrate the applicability of most of the IS-Impact measures. The analysis also shows a significant new measure having emerged from the context. This new measure was added as one of the System Quality measures. The second survey is a quantitative survey that aims to operationalise the measures identified from the qualitative analysis and rigorously validate the model. This survey was conducted in four state governments (including the state government that was involved in the first survey). A total of 254 valid responses were used in the data analysis. Data was analysed using structural equation modelling techniques, following the guidelines for formative construct validation, to test the validity and reliability of the constructs in the model. This study is the first research that extends the complete IS-Impact model in a new context that is different in terms of nationality, language and the type of information system (IS). The main contribution of this research is to present a comprehensive, up-to-date IS-Impact model, which has been validated in the new context. The study has accomplished its purpose of testing the generalisability of the IS-Impact model and continuing the IS evaluation research by extending it in the Malaysian context. A further contribution is a validated Malaysian language IS-Impact measurement instrument. It is hoped that the validated Malaysian IS-Impact instrument will encourage related IS research in Malaysia, and that the demonstrated model validity and generalisability will encourage a cumulative tradition of research previously not possible. The study entailed several methodological improvements on prior work, including: (1) new criterion measures for the overall IS-Impact construct employed in ‘identification through measurement relations’; (2) a stronger, multi-item ‘Satisfaction’ construct, employed in ‘identification through structural relations’; (3) an alternative version of the main survey instrument in which items are randomized (rather than blocked) for comparison with the main survey data, in attention to possible common method variance (no significant differences between these two survey instruments were observed); (4) demonstrates a validation process of formative indexes of a multidimensional, second-order construct (existing examples mostly involved unidimensional constructs); (5) testing the presence of suppressor effects that influence the significance of some measures and dimensions in the model; and (6) demonstrates the effect of an imbalanced number of measures within a construct to the contribution power of each dimension in a multidimensional model.

Non-invasive, quantitative analysis of drug mixtures in containers using spatially offset Raman spectroscopy (SORS) and multivariate statistical analysis

In this paper, spatially offset Raman spectroscopy (SORS) is demonstrated for non-invasively investigating the composition of drug mixtures inside an opaque plastic container. The mixtures consisted of three components including a target drug (acetaminophen or phenylephrine hydrochloride) and two diluents (glucose and caffeine). The target drug concentrations ranged from 5% to 100%. After conducting SORS analysis to ascertain the Raman spectra of the concealed mixtures, principal component analysis (PCA) was performed on the SORS spectra to reveal trends within the data. Partial least squares (PLS) regression was used to construct models that predicted the concentration of each target drug, in the presence of the other two diluents. The PLS models were able to predict the concentration of acetaminophen in the validation samples with a root-mean-square error of prediction (RMSEP) of 3.8% and the concentration of phenylephrine hydrochloride with an RMSEP of 4.6%. This work demonstrates the potential of SORS, used in conjunction with multivariate statistical techniques, to perform non-invasive, quantitative analysis on mixtures inside opaque containers. This has applications for pharmaceutical analysis, such as monitoring the degradation of pharmaceutical products on the shelf, in forensic investigations of counterfeit drugs, and for the analysis of illicit drug mixtures which may contain multiple components.

What explains the association between socioeconomic status and depression among Vietnamese adults?

Background Depression is a major public health problem worldwide and is currently ranked second to heart disease for years lost due to disability. For many decades, international research has found that depressive symptoms occur more frequently among low socioeconomic (SES) individuals than their more-advantaged peers. However, the reasons as to why those of low socioeconomic groups suffer more depressive symptoms are not well understood. Studies investigating the prevalence of depression and its association with SES emanate largely from developed countries, with little research among developing countries. In particular, there is a serious dearth of research on depression and no investigation of its association with SES in Vietnam. The aims of the research presented in this Thesis are to: estimate the prevalence of depressive symptoms among Vietnamese adults, examine the nature and extent of the association between SES and depression and to elucidate causal pathways linking SES to depressive symptoms Methods The research was conducted between September 2008 and November 2009 in Hue city in central Vietnam and used a combination of qualitative (in-depth interviews) and quantitative (survey) data collection methods. The qualitative study contributed to the development of the theoretical model and to the refinement of culturally-appropriate data collection instruments for the quantitative study. The main survey comprised a cross-sectional population–based survey with randomised cluster sampling. A sample of 1976 respondents aged between 25-55 years from ten randomly-selected residential zones (quarters) of Hue city completed the questionnaire (response rate 95.5%). Measures SES was classified using three indicators: education, occupation and income. The Center for Epidemiologic Studies-Depression (CES-D) scale was used to measure depressive symptoms (range0-51, mean=11.0, SD=8.5). Three cut-off points for the CES-D scores were applied: ‘at risk for clinical depression’ (16 or above), ‘depressive symptoms’ (above 21) and ‘depression’ (above 25). Six psychosocial indicators: life time trauma, chronic stress, recent life events, social support, self esteem, and mastery were hypothesized to mediate the association between SES and depressive symptoms. Analyses The prevalence of depressive symptoms were analysed using bivariate analyses. The multivariable analytic phase comprised of ordinary least squares regression, in accordance with Baron and Kenny’s three-step framework for mediation modeling. All analyses were adjusted for a range of confounders, including age, marital status, smoking, drinking and chronic diseases and the mediation models were stratified by gender. Results Among these Vietnamese adults, 24.3% were at or above the cut-off for being ‘at risk for clinical depression’, 11.9% were classified as having depressive symptoms and 6.8% were categorised as having depression. SES was inversely related to depressive symptoms: the least educated those with low occupational status or with the lowest incomes reported more depressive symptoms. Socioeconomicallydisadvantaged individuals were more likely to report experiencing stress (life time trauma, chronic stress or recent life events), perceived less social support and reported fewer personal resources (self esteem and mastery) than their moreadvantaged counterparts. These psychosocial resources were all significantly associated with depressive symptoms independent of SES. Each psychosocial factor showed a significant mediating effect on the association between SES and depressive symptoms. This was found for all measures of SES, and for males and females. In particular, personal resources (mastery, self esteem) and chronic stress accounted for a substantial proportion of the variation in depressive symptoms between socioeconomic groups. Social support and recent life events contributed modestly to socioeconomic differences in depressive symptoms, whereas lifetime trauma contributed the least to these inequalities. Conclusion This is the first known study in Vietnam or any developing country to systematically examine the extent to which psychosocial factors mediate the relationship between SES and depression. The study contributes new evidence regarding the burden of depression in Vietnam. The findings have practical relevance for advocacy, for mental health promotion and health-care services, and point to the need for programs that focus on building a sense of personal mastery and self esteem. More broadly, the work presented in this Thesis contributes to the international scientific literature on the social determinants of depression.

Anomalous subdiffusion equations by the meshless collocation method

This paper aims to develop an implicit meshless collocation technique based on the moving least squares approximation for numerical simulation of the anomalous subdiffusion equation(ASDE). The discrete system of equations is obtained by using the MLS meshless shape functions and the meshless collocation formulation. The stability and convergence of this meshless approach related to the time discretization are investigated theoretically and numerically. The numerical examples with regular and irregular nodal distributions are used to the newly developed meshless formulation. It is concluded that the present meshless formulation is very effective for the modeling of ASDEs.