Latent variable models of need for uniqueness

The theory of uniqueness has been invoked to explain attitudinal and behavioral nonconformity with respect to peer-group, social-cultural, and statistical norms, as well as the development of a distinctive view of self via seeking novelty goods, adopting new products, acquiring scarce commodities, and amassing material possessions. Present research endeavors in psychology and consumer behavior are inhibited by uncertainty regarding the psychometric properties of the Need for Uniqueness Scale, the primary instrument for measuring individual differences in uniqueness motivation. In an important step toward facilitating research on uniqueness motivation, we used confirmatory factor analysis to evaluate three a priori latent variable models of responses to the Need for Uniqueness Scale. Among the a priori models, an oblique three-factor model best accounted for commonality among items. Exploratory factor analysis followed by estimation of unrestricted three- and four-factor models revealed that a model with a complex pattern of loadings on four modestly correlated factors may best explain the latent structure of the Need for Uniqueness Scale. Additional analyses evaluated the associations among the three a priori factors and an array of individual differences. Results of those analyses indicated the need to distinguish among facets of the uniqueness motive in behavioral research.

Performance of the Giant Reed (Arundo donax) in experimental wetlands receiving variable loads of industrial stormwater

Two emergent macrophytes, Arundo donax and Phragmites australis, were established in experimental subsurface flow, gravel-based constructed wetlands (CWs) and challenged by untreated stormwater collected from the hard-pan and other surfaces of a dairy processing factory in south-west Victoria, Australia. The hydraulic loading rate was tested at two levels, sequentially, 3.75 and 7.5 cm day -1. Some of the monitored variables were removed more efficiently by the planted beds in comparison to unplanted CWs (biochemical oxygen demand (BOD), total nitrogen (TN) and total phosphorus (TP); p<0.007) but there was no significant difference between the A. donax and P. australis CWs in removal of BOD, suspended solids (SS) and TN (p>0.007) at 3.75 cm day -1 or SS and TN at 7.5 cm day -1. At 3.75 cm day -1, BOD, SS, TN and TP removal in the A. donax and P. australis CWs was 71%, 61%, 78% and 75% and 65%, 60%, 73% and 41%, respectively. Nutrient removal at 7.5 cm day -1 in the A. donax and P. australis beds was 87%, 91%, 84% and 71% and 96%, 94%, 87% and 55%, respectively. As expected, the A. donax CWs produced considerably more biomass (10±1.2 kg wet weight) than the P. australis CWs (2.7±1.2 kg wet weight). This equates to approximately 107 and 36 tonnes ha -1 year -1 biomass (dry weight) for A. donax and P. australis, respectively (assuming 250 days of growing season and singlecut harvest). The performance similarity of the A. donax- and P. australis-planted CWs indicates that either may be used in HSSF wetlands treating dairy factory stormwater, although the planting of A. donax provides additional opportunities for secondary income streams through utilisation of the biomass produced.

Reliable aggregation on network traffic for web based knowledge discovery

The web is a rich resource for information discovery, as a result web mining is a hot topic. However, a reliable mining result depends on the reliability of the data set. For every single second, the web generate huge amount of data, such as web page requests, file transportation. The data reflect human behavior in the cyber space and therefore valuable for our analysis in various disciplines, e.g. social science, network security. How to deposit the data is a challenge. An usual strategy is to save the abstract of the data, such as using aggregation functions to preserve the features of the original data with much smaller space. A key problem, however is that such information can be distorted by the presence of illegitimate traffic, e.g. botnet recruitment scanning, DDoS attack traffic, etc. An important consideration in web related knowledge discovery then is the robustness of the aggregation method , which in turn may be affected by the reliability of network traffic data. In this chapter, we first present the methods of aggregation functions, and then we employe information distances to filter out anomaly data as a preparation for web data mining.

Aggregation of preferences in recommender systems

This chapter gives an overview of aggregation functions toward their use in recommender systems. Simple aggregation functions such as the arithmetic mean are often employed to aggregate user features, item ratings, measures of similarity, etc., however many other aggregation functions exist which could deliver increased accuracy and flexibility to many systems. We provide definitions of some important families and properties, sophisticated methods of construction, and various examples of aggregation functions in the domain of recommender systems.

Labour productivity measurement with variable returns to scale in Australia's construction industry

As an important productivity indicator, the change of labour productivity is one indispensable marker in determining the rise or fall of overall industrial performance. This study aims to address whether the labour productivity level of the Australian construction industry has, in fact, shown a huge improvement during the last few decades. This article constructs a measuring method estimating labour productivity changes based on the data envelopment analysis technique with variable returns to scale. By adopting a production frontier approach, the labour productivity index can be broken down into components attributable to efficiency change, technological progress and capital accumulation. The numerical results exemplified by a single-input and single-output system indicate that the average annual labour productivity levels of the construction industry are slowly growing in all the Australian states and territories. However, the year-on-year change in the overall labour productivity performance does not maintain a long-term increase over the period 1990–2008. The study forms the basis for further industrial productivity research. Proposals and recommendations are expected to be beneficial for making policy and strategic decisions to improve the performance of the construction industry.

Aggregation for Atanassov's intuitionistic and interval valued fuzzy sets: the median operator

Atanassov's intuitionistic fuzzy sets (AIFS) and interval valued fuzzy sets (IVFS) are two generalizations of a fuzzy set, which are equivalent mathematically although different semantically. We analyze the median aggregation operator for AIFS and IVFS. Different mathematical theories have lead to different definitions of the median operator. We look at the median from various perspectives: as an instance of the intuitionistic ordered weighted averaging operator, as a Fermat point in a plane, as a minimizer of input disagreement, and as an operation on distributive lattices. We underline several connections between these approaches and summarize essential properties of the median in different representations.

Predicting protein functions from PPI networks using functional aggregation

Predicting protein functions computationally from massive protein–protein interaction (PPI) data generated by high-throughput technology is one of the challenges and fundamental problems in the post-genomic era. Although there have been many approaches developed for computationally predicting protein functions, the mutual correlations among proteins in terms of protein functions have not been thoroughly investigated and incorporated into existing prediction methods, especially in voting based prediction methods. In this paper, we propose an innovative method to predict protein functions from PPI data by aggregating the functional correlations among relevant proteins using the Choquet-Integral in fuzzy theory. This functional aggregation measures the real impact of each relevant protein function on the final prediction results, and reduces the impact of repeated functional information on the prediction. Accordingly, a new protein similarity and a new iterative prediction algorithm are proposed in this paper. The experimental evaluations on real PPI datasets demonstrate the effectiveness of our method.