On the weak monotonicity of Gini means and other mixture functions

Weak monotonicity was recently proposed as a relaxation of the monotonicity condition for averaging aggregation, and weakly monotone functions were shown to have desirable properties when averaging data corrupted with outliers or noise. We extended the study of weakly monotone averages by analyzing their ϕ-transforms, and we established weak monotonicity of several classes of averaging functions, in particular Gini means and mixture operators. Mixture operators with Gaussian weighting functions were shown to be weakly monotone for a broad range of their parameters. This study assists in identifying averaging functions suitable for data analysis and image processing tasks in the presence of outliers.

Typology of alcohol mixed with energy drink consumers: motivations for use

Background: Previous research on alcohol mixed with energy drinks (AmED) has shown that use is typically driven by hedonistic, social, functional, and intoxication-related motives, with differential associations with alcohol-related harm across these constructs. There has been no research looking at whether there are subgroups of consumers based on patterns of motivations. Consequently, the aims were to determine the typology of motivations for AmED use among a community sample and to identify correlates of subgroup membership. In addition, we aimed to determine whether this structure of motivations applied to a university student sample. Methods: Data were used from an Australian community sample (n = 731) and an Australian university student sample (n = 594) who were identified as AmED consumers when completing an online survey about their alcohol and ED use. Participants reported their level of agreement with 14 motivations for AmED use; latent classes of AmED consumers were identified based on patterns of motivation endorsement using latent class analysis. Results: A 4-class model was selected using data from the community sample: (i) taste consumers (31%): endorsed pleasurable taste; (ii) energy-seeking consumers (24%): endorsed functional and taste motives; (iii) hedonistic consumers (33%): endorse pleasure and sensation-seeking motives, as well as functional and taste motives; and (iv) intoxication-related consumers (12%): endorsed motives related to feeling in control of intoxication, as well as hedonistic, functional, and taste motives. The consumer subgroups typically did not differ on demographics, other drug use, alcohol and ED use, and AmED risk taking. The patterns of motivations for the 4-class model were similar for the university student sample. Conclusions: This study indicated the existence of 4 subgroups of AmED consumers based on their patterns of motivations for AmED use consistently structured across the community and university student sample. These findings lend support to the growing conceptualization of AmED consumers as a heterogeneous group in regard to motivations for use, with a hierarchical and cumulative class order in regard to the number of types of motivation for AmED use. Prospective research may endeavor to link session-specific motives and outcomes, as it is apparent that primary consumption motives may be fluid between sessions.

A longitudinal study of the association of adolescent polydrug use, alcohol use and high school non-completion

AIMS: Failure to complete high school predicts substantial economic and social disadvantage in adult life. The aim of this study was to determine the longitudinal association of mid-adolescent polydrug use and high school non-completion, relative to other drug use profiles. DESIGN: A longitudinal analysis of the relationship between polydrug use in three cohorts at grade 9 (age 14-15 years) and school non-completion (reported post-high school). SETTING: A State-representative sample of students across Victoria, Australia. PARTICIPANTS: A total of 2287 secondary school students from 152 high schools. The retention rate was 85%. MEASUREMENTS: The primary outcome was non-completion of grade 12 (assessed at age 19-23 years). At grade 9, predictors included 30-day use of eight drugs, school commitment, academic failure and peer drug use. Other controls included socio-economic status, family relationship quality, depressive symptoms, gender, age and cohort. FINDINGS: Three distinct classes of drug use were identified-no drug use (31.7%), mainly alcohol use (61.8%) and polydrug use (6.5%). Polydrug users were characterized by high rates of alcohol, tobacco and cannabis use. In the full model, mainly alcohol users and polydrug users were less likely to complete school than non-drug users [odds ratio (OR) = 1.54, 95% confidence interval (CI) = 1.17-2.03) and OR = 2.51, 95% CI = 1.45-4.33), respectively, P < 0.001]. These effects were independent of school commitment, academic failure, peer drug use and other controls. CONCLUSIONS: Mid-adolescent polydrug use in Australia predicts subsequent school non-completion after accounting for a range of potential confounding factors. Adolescents who mainly consume alcohol are also at elevated risk of school non-completion.

Properties and automotive applications of advanced high-strength steels (AHSS)

Advanced high-strength steels (AHSS) are a class of steel used primarily in sheet form for automotive structures. The microstructures of the types of steel in this classification were initially multiphase, with ferrite as the dominant phase; however, grades introduced more recently have been fully martensitic or based on austenite. This chapter initially introduces the requirements of an automotive body structure, then the different classes of AHSS that have been used in the automotive industry and their typical characteristic tensile properties. The specific properties that are required for steel used in automotive body structures are subsequently described, including formability and crash behaviour. Finally, some of the current and future trends in the development of new steel grades are discussed.

A data as a product model for future consumption of big stream data in clouds

Data is becoming the world’s new natural resourceand big data use grows quickly. The trend of computingtechnology is that everything is merged into the Internet and‘big data’ are integrated to comprise completeinformation for collective intelligence. With the increasingsize of big data, refining big data themselves to reduce data sizewhile keeping critical data (or useful information) is a newapproach direction. In this paper, we provide a novel dataconsumption model, which separates the consumption of datafrom the raw data, and thus enable cloud computing for bigdata applications. We define a new Data-as-a-Product (DaaP)concept; a data product is a small sized summary of theoriginal data and can directly answer users’ queries. Thus, weseparate the mining of big data into two classes of processingmodules: the refine modules to change raw big data into smallsizeddata products, and application-oriented mining modulesto discover desired knowledge further for applications fromwell-defined data products. Our practices of mining big streamdata, including medical sensor stream data, streams of textdata and trajectory data, demonstrated the efficiency andprecision of our DaaP model for answering users’ queries