Digital Game Dynamics Preferences and Player Types

In this study, we examine digital game preferences by identifying game dynamics, i.e. player–game interaction modes, of 700 contemporary digital games, and players' (N = 1717) desire to play games with specific types of dynamics. Based on statistical analysis of the data, 5 game dynamics preference categories (“assault,” “manage,” “journey,” “care,” and “coordinate”) and 7 player types were revealed. The results show that identifying player types requires including both preferred and undesired game dynamics categories in the analysis. The findings unveil digital gaming as a more multifaceted phenomenon than common stereotypes suggest. The original game preferences model we present in this study can be conceptualized as a complementary approach for motivations to play and player behavior studies.     

Mobile crowdsensing with mobile agents

We introduce mobile agents for mobile crowdsensing. Crowdsensing campaigns are designed through different roles that are implemented as mobile agents. The role-based tasks of mobile agents include collecting data, analyzing data and sharing data in the campaign. Mobile agents execute and control the campaign autonomously as a multi-agent system and migrate in the opportunistic network of participants’ devices. Mobile agents take into account the available resources in the devices and match participants’ privacy requirements to the campaign requirements. Sharing of task results in real-time facilitates cooperation towards the campaign goal while maintaining a selected global measure, such as energy efficiency. We discuss current challenges in crowdsensing and propose mobile agent based solutions for campaign execution and monitoring, addressing data collection and participant-related issues. We present a software framework for mobile agents-based crowdsensing that is seamlessly integrated into the Web. A set of simulations are conducted to compare mobile agent-based campaigns with existing crowdsensing approaches. We implemented and evaluated a small-scale real-world mobile agent based campaign for pedestrian flock detection. The simulation and evaluation results show that mobile agent based campaigns produce comparable results with less energy consumption when the number of agents is relatively small and enables in-network data processing with sharing of data and task results with insignificant overhead.     

Temporal Kohonen Map and the Recurrent Self-Organizing Map: Analytical and Experimental Comparison

This paper compares two Self-Organizing Map (SOM) based models for temporal sequence processing (TSP) both analytically and experimentally. These models, Temporal Kohonen Map (TKM) and Recurrent Self-Organizing Map (RSOM), incorporate leaky integrator memory to preserve the temporal context of the input signals. The learning and the convergence properties of the TKM and RSOM are studied and we show analytically that the RSOM is a significant improvement over the TKM, because the RSOM allows simple derivation of a consistent learning rule. The results of the analysis are demonstrated with experiments.     

Analysing singularities of a benchmark problem

The purpose of this paper is to analyze the singularities of a well known benchmark problem “Andrews’ squeezing mechanism.” We show that for physically relevant parameter values this system admits singularities, and describe explicit conditions for the parameters. The method is based on Gröbner bases computations and ideal decomposition. It is algorithmic and can thus be applied to study constraint singularities which arise in more general situations as well.     

Application of evolutionary optimisers in data-based calibration of Activated Sludge Models

Modelling activated sludge systems has become an accepted practice in Wastewater Treatment Plant (WWTP) design, teaching and research, and Activated Sludge Models (ASM) are by far the most widely used models for activated sludge systems. In most ASM applications, calibration is based on more or less ad-hoc and trial and error approaches. Calibration of the ASMs remains the weakest link in the overall process of modelling biological wastewater treatment. In this paper, a calibration approach is proposed where the need for expert knowledge and modeller effort is significantly reduced. The calibration approach combines identifiability analysis and evolutionary optimisers to automate the ASM calibration. Identifiability analysis is used to deal with poor identifiability of the model structures and evolutionary optimisers are used to identify the model parameters. The applied evolutionary optimisers are Genetic Algorithms and Differential Evolution. Performance of the evolutionary optimisers is compared with a previously proposed calibration approach based on Monte Carlo simulations. All methods were capable of calibrating the model when given enough computation time. However, some of the evolutionary optimisation methods had an advantage in terms of computation time against the Monte Carlo method.     

Weight initialization with reference patterns

The problem of weight initialization in multilayer perceptron networks is considered. A new computationally simple weight initialization method based on the usage of reference patterns is presented. A reference pattern is a vector which is used to represent data points that fall in its vicinity in the data space. On one hand, the proposed method aims to set the initial weight values to be such that inputs to network nodes are within the active region (in other words, nodes are not saturated). On the other hand, the goal is to distribute the discriminant functions formed by the hidden units evenly into the input space area where training data is located. The proposed method is tested with the widely used two-spirals classification benchmark problem and channel equalization problem where several alternatives for obtaining suitable reference patterns are investigated. Also, the effect of the initialization is studied when two commonly used cost functions are used in the training phase. These are the mean square error and relative entropy cost functions. A comparison with the conventional random initialization shows that significant improvement in convergence can be achieved with the proposed method. In addition, the computational cost of the initialization was found to be negligible compared with the cost of training.     

Quantitative risk assessment on the dietary exposure of Finnish children and adults to nitrite

Nitrite intake from the consumption of cured meat and tap water was estimated for Finnish children of 1, 3 and 6 years as well as Finnish adults of 25–74 years. Nitrite content in the foods was measured by capillary electrophoresis, and was then used together with individual food consumption data from the FINDIET 2007 and DIPP studies in a stochastic exposure assessment by a Monte Carlo Risk Assessment (MCRA) program. Nitrite intake from additive sources and tap water was assessed, and more than every 10th child between the ages 3 and 6 years was estimated to have a nitrite intake exceeding the acceptable daily intake (ADI) of nitrite. The high exposure levels were caused by frequent consumption of large portions of sausages, up to 350 g day^–1 or 750 g in 3 days, among the children. Median nitrite intake from cured meat was 0.016, 0.040, 0.033 and 0.005 mg kg^–1 body weight day^–1 for children of 1, 3 and 6 years and adults, respectively. Bayesian estimation was employed to determine safe consumption levels of sausages and cold cuts for children, and these results gave rise to new national food consumption advice.     

The association of distress and sleeping problems with physicians’ intentions to change profession: The moderating effect of job control.

The present study examined whether job control moderated the association between stress indicators (distress and sleeping problems) and intentions to change profession among 2,650 Finnish physicians. Ordinal logistic regression analysis was applied. The authors found that high levels of distress and sleeping problems were associated with higher levels of intentions to change profession, whereas high job control was associated with lower levels of intentions to change profession even after adjusting for the effects of gender, age, and employment sector. In addition, high job control was able to mitigate the positive association that distress and sleeping problems had with intentions to change profession. Our findings highlight the importance of offering more job control to physicians to prevent unnecessary physician turnover. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Correction to: Embrittlement Analysis of Σ5[210]/(-1-20) FeAl Grain Boundary in Presence of Defects: An Ab Initio Study

Metallurgical and Materials Transactions A -     

Cellulose/acrylic acid copolymer blends for films and coating applications

Enzymatically treated cellulose was dissolved in a NaOH/ZnO solvent system and mixed together with poly(ethylene‐co‐acrylic acid) (PE‐co‐AA) or poly(acrylamide‐co‐acrylic acid) (PAA‐co‐AA) polymers, in order to improve the properties of dissolved cellulose and to prepare homogeneous cellulose‐based blends for films and coatings. The solution stage properties of the blends were evaluated by rheological methods and the precipitated dry blends were characterized by dynamic mechanical analysis, differential scanning calorimetry, and scanning electron microscopy. Paperboard coating tests done at laboratory scale showed dissolved cellulose/acrylic acid copolymer‐based blends function well as coating materials. All of the tested blends showed a good resistance against grease in the coating trials, having grease resistance from 60 to 69 days despite a very thin (～2 µm) coating layer. In addition, cellulose/PE‐co‐AA coating showed improved water vapor and oxygen barrier properties when compared with neat dissolved cellulose‐coated paperboard. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 40286.