The role of desire in understanding intentions to drink responsibly : an application of the model of goal-directed behaviour

This study models young people's moderate drinking decision-making using the Model of Goal-Directed Behaviour (MGB), thus presenting insights into young people's desires and intentions to drink responsibly. Testing the applicability of the MGB to quantitatively analyse responsible drinking, the explanatory sphere of the MGB is extended. An online survey resulted in 1522 completed questionnaires from respondents aged between 18 and 25 years. Collected data were analysed with structural equation modelling (SEM) using SPSS AMOS21 (IBM, New York, NY, USA) software. The key finding of this study is that an individual's desire to drink moderately is the most important predictor of young people's responsible drinking intentions. Our use of MGB provides further evidence that there is a strong distinction between consumer desires and intentions.

Thermal stability and decomposition kinetics of styrene-butadiene rubber nanocomposites filled with different particle sized kaolinites

A series of styrene-butadiene rubber (SBR) nanocomposites filledwith different particle sized kaolinites are prepared via a latex blending method. The thermal stabilities of these clay polymer nanocomposites (CPN) are characterized by a range of techniques including thermogravimetry (TG), digital photos, scanning electron microscopy (SEM) and Raman spectroscopy. These CPN show some remarkable improvement in thermal stability compared to that of the pure SBR. With the increase of kaolinite particle size, the residual char content and the average activation energy of kaolinite SBR nanocomposites all decrease; the pyrolysis residues become porous; the crystal carbon in the pyrolysis residues decrease significantly from 58.23% to 44.41%. The above results prove that the increase of kaolinite particle size is not beneficial in improving the thermal stability of kaolinite SBR nanocomposites.

Degradation modeling and monitoring of machines using operation-specific hidden Markov models

In this paper, a novel data-driven approach to monitoring of systems operating under variable operating conditions is described. The method is based on characterizing the degradation process via a set of operation-specific hidden Markov models (HMMs), whose hidden states represent the unobservable degradation states of the monitored system while its observable symbols represent the sensor readings. Using the HMM framework, modeling, identification and monitoring methods are detailed that allow one to identify a HMM of degradation for each operation from mixed-operation data and perform operation-specific monitoring of the system. Using a large data set provided by a major manufacturer, the new methods are applied to a semiconductor manufacturing process running multiple operations in a production environment.

Nonlinear interactions of azimuthal surface waves in magnetoactive plasma filled waveguides

This paper deals with the theoretical studies of nonlinear interactions of azimuthal surface waves (ASW) in cylindrical metal waveguides fully filled by a uniform magnetoactive plasma. These surface-type wave perturbations propagate in azimuthal direction across an external magnetic field, which is directed along the waveguide axis. The ASW is a relatively new kind of surface waves and so far the nonlinear effects associated with their propagation are outside the scope of scientific issues. They are characterized by a discrete set of mode numbers values which define the ASW eigenfrequencies. This fact leads to several peculiarities of ASW compared with ordinary surface-type waves.

A random key genetic algorithm for live migration of multiple virtual machines in data centers

Live migration of multiple Virtual Machines (VMs) has become an integral management activity in data centers for power saving, load balancing and system maintenance. While state-of-the-art live migration techniques focus on the improvement of migration performance of an independent single VM, only a little has been investigated to the case of live migration of multiple interacting VMs. Live migration is mostly influenced by the network bandwidth and arbitrarily migrating a VM which has data inter-dependencies with other VMs may increase the bandwidth consumption and adversely affect the performances of subsequent migrations. In this paper, we propose a Random Key Genetic Algorithm (RKGA) that efficiently schedules the migration of a given set of VMs accounting both inter-VM dependency and data center communication network. The experimental results show that the RKGA can schedule the migration of multiple VMs with significantly shorter total migration time and total downtime compared to a heuristic algorithm.

Experimental and numerical study of polymeric foam efficacy in portable water filled barriers

Portable, water filled road safety barriers are used to provide protection and reduce the potential hazard due to errant vehicles in areas where the road conditions change frequently (e.g. near road work sites). As part of an effort to reduce excessive working widths typical of these systems, a study was conducted to assess the effectiveness of introducing polymeric foam filled panels into the design. Surrogate impact tests of a design typical of such as barrier system were conducted utilising a pneumatically powered horizontal impact testing machine up to impact energies of 7.40 kJ. Results of these tests are utilised to examine the barrier behaviour, in addition to being used to validate a couple FE/SPH model of the barrier system. Once validated, the FE/SPH model it utilised as the basis for a parametric study into the efficacy and effects of the inclusion of polymeric foam filled panels on the performance of portable water filled road safety barriers. It was found that extruded polystyrene foam functioned well, with a greater thickness of the foam panel significantly reducing the impacting body velocity as the barrier began to translate.

Performance of new-generation portable water-filled road safety barriers under vehicular impact

Portable water-filled barriers (PWFB) are roadside structures used to enhance safety at roadside work-zones. Ideally, a PWFB system is expected to protect persons and objects behind it and redirect the errant vehicle. The performance criteria of a road safety barrier system are (i) redirection of the vehicle after impact and (ii) lateral deflection within allowable limits. Since its inception, the PWFB has received criticism due to its underperformance compared to the heavier portable concrete barrier. A new generation composite high energy absorbing road safety barrier was recently developed by the authors.

Pedagogy of Desire

Despite major inroads in demystifying creativity for the non-design disciplines, there has been little movement in the design disciplines themselves beyond traditional paradigms. This is particularly noticeable in design education where traditional pedagogical approaches persist despite the emergence of new experimental pedagogies. In response, this research aims to explore what a pedagogy of desire can offer and what this means in terms of curriculum development; learning environments; teaching approaches and staff development. Specifically, it seeks to: understand more fully the notion of desire and how students’ and teachers’ desires can be exploited in creative and productive ways; to further explore the relationship between risk (through experiencing uncertainties and anxieties) and pleasure (through assuming the subversive position of knowing); to identify and explore how to negotiate personal, professional and organisational implications; and to develop appropriate evaluation mechanisms.

The elaborated intrusion theory of desire : a 10-year retrospective and implications for addiction treatments

Introduction: Ten years after the publication of Elaborated Intrusion (EI) Theory, there is now substantial research into its key predictions. The distinction between intrusive thoughts, which are driven by automatic processes, and their elaboration, involving controlled processing, is well established. Desires for both addictive substances and other desired targets are typically marked by imagery, especially when they are intense. Attention training strategies such as body scanning reduce intrusive thoughts, while concurrent tasks that introduce competing sensory information interfere with elaboration, especially if they compete for the same limited-capacity working memory resources. Conclusion: EI Theory has spawned new assessment instruments that are performing strongly and offer the ability to more clearly delineate craving from correlated processes. It has also inspired new approaches to treatment. In particular, training people to use vivid sensory imagery for functional goals holds promise as an intervention for substance misuse, since it is likely to both sustain motivation and moderate craving.

The Craving Experience Questionnaire : a brief, theory-based measure of consummatory desire and craving

Background and Aims Research into craving is hampered by lack of theoretical specification and a plethora of substance-specific measures. This study aimed to develop a generic measure of craving based on elaborated intrusion (EI) theory. Confirmatory factor analysis (CFA) examined whether a generic measure replicated the three-factor structure of the Alcohol Craving Experience (ACE) scale over different consummatory targets and time-frames. Design Twelve studies were pooled for CFA. Targets included alcohol, cigarettes, chocolate and food. Focal periods varied from the present moment to the previous week. Separate analyses were conducted for strength and frequency forms. Setting Nine studies included university students, with single studies drawn from an internet survey, a community sample of smokers and alcohol-dependent out-patients. Participants A heterogeneous sample of 1230 participants. Measurements Adaptations of the ACE questionnaire. Findings Both craving strength [comparative fit indices (CFI = 0.974; root mean square error of approximation (RMSEA) = 0.039, 95% confidence interval (CI) = 0.035–0.044] and frequency (CFI = 0.971, RMSEA = 0.049, 95% CI = 0.044–0.055) gave an acceptable three-factor solution across desired targets that mapped onto the structure of the original ACE (intensity, imagery, intrusiveness), after removing an item, re-allocating another and taking intercorrelated error terms into account. Similar structures were obtained across time-frames and targets. Preliminary validity data on the resulting 10-item Craving Experience Questionnaire (CEQ) for cigarettes and alcohol were strong. Conclusions The Craving Experience Questionnaire (CEQ) is a brief, conceptually grounded and psychometrically sound measure of desires. It demonstrates a consistent factor structure across a range of consummatory targets in both laboratory and clinical contexts.

Onset of transition in mixed convection of a lid-driven trapezoidal enclosure filled with water-Al2O3 nanofluid

A numerical study is carried out to investigate the transition from laminar to chaos in mixed convection heat transfer inside a lid-driven trapezoidal enclosure. In this study, the top wall is considered as isothermal cold surface, which is moving in its own plane at a constant speed, and a constant high temperature is provided at the bottom surface. The enclosure is assumed to be filled with water-Al2O3 nanofluid. The governing Navier–Stokes and thermal energy equations are expressed in non-dimensional forms and are solved using Galerkin finite element method. Attention is paid in the present study on the pure mixed convection regime at Richandson number, Ri = 1. The numerical simulations are carried out over a wide range of Reynolds (0.1 ≤ Re ≤ 103) and Grashof (0.01 ≤ Gr ≤ 106) numbers. Effects of the presence of nanofluid on the characteristics of mixed convection heat transfer are also explored. The average Nusselt numbers of the heated wall are computed to demonstrate the influence of flow parameter variations on heat transfer. The corresponding change of flow and thermal fields is visualized from the streamline and the isotherm contour plots.

Classification of echolocation calls from 14 species of bat by Support Vector Machines and Ensembles of Neural Networks

Calls from 14 species of bat were classified to genus and species using discriminant function analysis (DFA), support vector machines (SVM) and ensembles of neural networks (ENN). Both SVMs and ENNs outperformed DFA for every species while ENNs (mean identification rate – 97%) consistently outperformed SVMs (mean identification rate – 87%). Correct classification rates produced by the ENNs varied from 91% to 100%; calls from six species were correctly identified with 100% accuracy. Calls from the five species of Myotis, a genus whose species are considered difficult to distinguish acoustically, had correct identification rates that varied from 91 – 100%. Five parameters were most important for classifying calls correctly while seven others contributed little to classification performance.

Reliable Fault Diagnosis for Low-Speed Bearings Using Individually Trained Support Vector Machines With Kernel Discriminative Feature Analysis

This paper proposes a highly reliable fault diagnosis approach for low-speed bearings. The proposed approach first extracts wavelet-based fault features that represent diverse symptoms of multiple low-speed bearing defects. The most useful fault features for diagnosis are then selected by utilizing a genetic algorithm (GA)-based kernel discriminative feature analysis cooperating with one-against-all multicategory support vector machines (OAA MCSVMs). Finally, each support vector machine is individually trained with its own feature vector that includes the most discriminative fault features, offering the highest classification performance. In this study, the effectiveness of the proposed GA-based kernel discriminative feature analysis and the classification ability of individually trained OAA MCSVMs are addressed in terms of average classification accuracy. In addition, the proposedGA- based kernel discriminative feature analysis is compared with four other state-of-the-art feature analysis approaches. Experimental results indicate that the proposed approach is superior to other feature analysis methodologies, yielding an average classification accuracy of 98.06% and 94.49% under rotational speeds of 50 revolutions-per-minute (RPM) and 80 RPM, respectively. Furthermore, the individually trained MCSVMs with their own optimal fault features based on the proposed GA-based kernel discriminative feature analysis outperform the standard OAA MCSVMs, showing an average accuracy of 98.66% and 95.01% for bearings under rotational speeds of 50 RPM and 80 RPM, respectively.

Mechanical behavior of circular and square concrete filled steel tube stub columns under local compression

This paper presents a combined experimental and numerical study on the behaviour of both circular and square concrete-filled steel tube (CFT) stub columns under local compression. Twelve circular and eight square CFT stub columns were tested to study their bearing capacity and the key influential parameters. A 3D finite element model was established for simulation and parametric study to investigate the structural behaviour of the stub columns. The numerical results agreed well with the experimental results. In addition, analytical formulas were proposed to calculate the load bearing capacity of CFT stub columns under local compression.