Is compulsive internet use related to sensitivity to reward and punishment,and impulsivity?

Aim of the present study was to examine whether the personality correlates sensitivity to reward and to punishment, and impulsivity predict compulsive internet use (CIU). Furthermore, the predictive value of these personality correlates was compared to the predictive value of factors relating to psychosocial wellbeing. The results showed that particularly rash spontaneous impulsivity predicts CIU and that this personality factor is more important than psychosocial wellbeing factors. Sensitivity to reward, which is supposed to play a role in craving processes associated with substance abuse and eating disorders, could not be related to CIU. The data suggest that internet users who are characterized by an impulsive personality feature, are less able to control their use of the internet, which makes them more vulnerable to develop CIU.     

Influence of Matrices on Oxygen Sensing of Three Sensing Films with Chemically Conjugated Platinum Porphyrin Probes and Preliminary Application for Monitoring of Oxygen Consumption of Escherichia coli (E. coli)

Oxygen sensing films were synthesized by a chemical conjugation of functional platinum porphyrin probes in silica gel, polystyrene (PS), and poly(2-hydroxyethyl methacrylate) (PHEMA) matrices. Responses of the sensing films to gaseous oxygen and dissolved oxygen were studied and the influence of the matrices on the sensing behaviors was investigated. Silica gel films had the highest fluorescence intensity ratio from deoxygenated to oxygenated environments and the fastest response time to oxygen. PHEMA films had no response to gaseous oxygen, but had greater sensitivity and a faster response time for dissolved oxygen than those of PS films. The influence of matrices on oxygen response, sensitivity and response time was discussed. The influence is most likely attributed to the oxygen diffusion abilities of the matrices. Since the probes were chemically immobilized in the matrices, no leaching of the probes was observed from the sensing films when applied in aqueous environment. One sensing film made from the PHEMA matrix was used to preliminarily monitor the oxygen consumption of Escherichia coli (E. coli) bacteria. E. coli cell density and antibiotics ampicillin concentration dependent oxygen consumption was observed, indicating the potential application of the oxygen sensing film for biological application.     

Evaluation of different disinfection calculation methods using CFD

Computational Fluid Dynamics combined with a particle tracking technique provides valuable information concerning residence times and contact times in chemical reactors. In drinking water treatment, for example an accurate estimation of the disinfection is important to predict the microbial safety. Ozone contactors are widely used for disinfection, but the complex geometry of the system causes suboptimal hydraulics and requires optimizations of the flow. This results in a lower ozone dosage, which may reduce the formation of unwanted disinfection-by-products and the consumption of energy. To that end disinfection needs to be calculated precisely, accounting for the complex hydraulics. Several calculation methods estimating the disinfection performance of ozone contactors were evaluated using Computational Fluid Dynamics. For an accurate disinfection prediction, the full distribution of ozone exposures (CT values) is needed, only a mean CT value or residence time distribution provides insufficient information for an accurate disinfection prediction. Adjustments to the geometry of the ozone contactor that reduce the short-circuit flows resulted in an increase in disinfection capacity, whereas the mean CT value remained the same. A sensitivity analysis with respect to the kinetics was conducted. The gain in disinfection capacity obtained by optimizing the hydraulics was significant for typical values used in practice.     

APROMORE: An advanced process model repository

Business process models are becoming available in large numbers due to their widespread use in many industrial applications such as enterprise and quality engineering projects. On the one hand, this raises a challenge as to their proper management: how can it be ensured that the proper process model is always available to the interested stakeholder? On the other hand, the richness of a large set of process models also offers opportunities, for example with respect to the re-use of existing model parts for new models. This paper describes the functionality and architecture of an advanced process model repository, named APROMORE. This tool brings together a rich set of features for the analysis, management and usage of large sets of process models, drawing from state-of-the art research in the field of process modeling. A prototype of the platform is presented in this paper, demonstrating its feasibility, as well as an outlook on the further development of APROMORE.     

Model predictive control for systems with stochastic multiplicative uncertainty and probabilistic constraints

Robust predictive control handles constrained systems that are subject to stochastic uncertainty but propagating the effects of uncertainty over a prediction horizon can be computationally expensive and conservative. This paper overcomes these issues through an augmented autonomous prediction formulation, and provides a method of handling probabilistic constraints and ensuring closed loop stability through the use of an extension of the concept of invariance, namely invariance with probability p.     

Suppressing intersample behavior in iterative learning control

Iterative Learning Control (ILC) is a control strategy to improve the performance of digital batch repetitive processes. Due to its digital implementation, discrete time ILC approaches do not guarantee good intersample behavior. In fact, common discrete time ILC approaches may deteriorate the intersample behavior, thereby reducing the performance of the sampled-data system. In this paper, a generally applicable multirate ILC approach is presented that enables to balance the at-sample performance and the intersample behavior. Furthermore, key theoretical issues regarding multirate systems are addressed, including the time-varying nature of the multirate ILC setup. The proposed multirate ILC approach is shown to outperform discrete time ILC in realistic simulation examples.     

Combining feature spaces for classification

In this paper we offer a variational Bayes approximation to the multinomial probit model for basis expansion and kernel combination. Our model is well-founded within a hierarchical Bayesian framework and is able to instructively combine available sources of information for multinomial classification. The proposed framework enables informative integration of possibly heterogeneous sources in a multitude of ways, from the simple summation of feature expansions to weighted product of kernels, and it is shown to match and in certain cases outperform the well-known ensemble learning approaches of combining individual classifiers. At the same time the approximation reduces considerably the CPU time and resources required with respect to both the ensemble learning methods and the full Markov chain Monte Carlo, Metropolis-Hastings within Gibbs solution of our model. We present our proposed framework together with extensive experimental studies on synthetic and benchmark datasets and also for the first time report a comparison between summation and product of individual kernels as possible different methods for constructing the composite kernel matrix.     

Reduction rules for YAWL workflows with cancellation regions and OR-joins

As the need for concepts such as cancellation and OR-joins occurs naturally in business scenarios, comprehensive support in a workflow language is desirable. However, there is a clear trade-off between the expressive power of a language (i.e., introducing complex constructs such as cancellation and OR-joins) and ease of verification. When a workflow contains a large number of tasks and involves complex control flow dependencies, verification can take too much time or it may even be impossible. There are a number of different approaches to deal with this complexity. Reducing the size of the workflow, while preserving its essential properties with respect to a particular analysis problem, is one such approach. In this paper, we present a set of reduction rules for workflows with cancellation regions and OR-joins and demonstrate how they can be used to improve the efficiency of verification. Our results are presented in the context of the YAWL workflow language.