Search results for "pää"

Data-driven analysis for fMRI during naturalistic music listening

2017

Interest towards higher ecological validity in functional magnetic resonance imaging (fMRI) experiments has been steadily growing since the turn of millennium. The trend is reflected in increasing amount of naturalistic experiments, where participants are exposed to the real-world complex stimulus and/or cognitive tasks such as watching movie, playing video games, or listening to music. Multifaceted stimuli forming parallel streams of input information, combined with reduced control over experimental variables introduces number of methodological challenges associated with isolating brain responses to individual events. This exploratory work demonstrated some of those methodological challeng…

Making and breaking relationships on social media: the impacts of brand and influencer betrayals

2021

This study considers how the relationships between social media influencers, brands and individuals are intertwined on social media and analyses the spill-over effects of feelings of betrayal. An experimental design with two transgression scenarios (influencer vs. brand) was created, and 250 individuals were recruited to participate in the study. The results show that a perceived betrayal by a brand can negatively affect the perceived coolness of the social media influencer that has endorsed the brand, as well as the parasocial relationships that followers have with the influencer. Accordingly, a perceived betrayal by a social media influencer can negatively affect attitudes, trust and purc…

Predicting consumer purchase intention toward hybrid vehicles : testing the moderating role of price sensitivity

2020

PurposeToday, global warming is one of the most acute challenges in the world, prominently caused by greenhouse gases. The introduction of hybrid-vehicles (HVs) is thus, one of the industrial initiatives to tackle this challenge by allowing at least some proportionate reduction in global-gas-emissions. Such initiatives like HVs have also affected the consumers’ green-purchase-intention (GPI). Hence, underpinned into the theory of planned behaviour (TPB), this study aims to analyze consumers’ response in terms of GPI for HVs, in addition to exploring the moderating-effect of price-sensitivity between independent-variables (attitude, subjective norms and perceived behavioural control) and con…

Der pikareske Roman als Katalysator in geschichtlichen Abläufen : erzählerische Kommunikationsmodelle in Das Leben des Lazarillo von Tormes, bei Thom…

2005

Anna-Riitta Tunturi tutki väitöskirjassaan pikareski- eli veijariromaanin eri ilmenemismuotoja historiallisena läpileikkauksena, sosiologisena ilmiönä ja romaanitaiteen osa-alueena. Sana ”pícaro” tulee kastiliankielen verbistä ”picar”, joka tarkoittaa alempiarvoisten ammattien harjoittamista, kuten picador de toros, pikadori härkätaistelussa, toreadorin avustaja. Saksaksi sana on Schelm, joten oma sanamme kelmi on saksalaista lainaa. Lisäksi suomen kielessä on veijari, huijari, heittiö tai hulttio, ja lisääkin varmasti löytyy. This dissertation in comparative literature examines several Finnish picaresque novels of 20th century in relation to some classical continental European novels such …

Pajunen

1818

Interactive effects of parasitism and predation on the behaviour of the amphipod Pallasea quadrispinosa

2006

ANOVA-MOP: ANOVA Decomposition for Multiobjective Optimization

2018

Real-world optimization problems may involve a number of computationally expensive functions with a large number of input variables. Metamodel-based optimization methods can reduce the computational costs of evaluating expensive functions, but this does not reduce the dimension of the search domain nor mitigate the curse of dimensionality effects. The dimension of the search domain can be reduced by functional anova decomposition involving Sobol' sensitivity indices. This approach allows one to rank decision variables according to their impact on the objective function values. On the basis of the sparsity of effects principle, typically only a small number of decision variables significantl…

Potential of interactive multiobjective optimization in supporting the design of a groundwater biodenitrification process

2019

The design of water treatment plants requires simultaneous analysis of technical, economic and environmental aspects, identified by multiple conflicting objectives. We demonstrated the advantages of an interactive multiobjective optimization (MOO) method over a posteriori methods in an unexplored field, namely the design of a biological treatment plant for drinking water production, that tackles the process drawbacks, contrarily to what happens in a traditional volumetric-load-driven design procedure. Specifically, we consider a groundwater denitrification biofilter, simulated by the Activated Sludge Model modified with two-stage denitrification kinetics. Three objectives were defined (nitr…

Simultaneous optimization of harvest schedule and data quality

2015

In many recent studies, the value of forest inventory information in harvest scheduling has been examined. In a previous paper, we demonstrated that making measurement decisions for stands for which the harvest decision is uncertain simultaneously with the harvest decisions may be highly profitable. In that study, the quality of additional measurements was not a decision variable, and the only options were between making no measurements or measuring perfect information. In this study, we introduce data quality into the decision problem, i.e., the decisionmaker can select between making imperfect or perfect measurements. The imperfect information is obtained with a specific scenario tree fo…

A Surrogate-assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-objective Optimization

2018

We propose a surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive optimization problems with more than three objectives. The proposed algorithm is based on a recently developed evolutionary algorithm for many-objective optimization that relies on a set of adaptive reference vectors for selection. The proposed surrogateassisted evolutionary algorithm uses Kriging to approximate each objective function to reduce the computational cost. In managing the Kriging models, the algorithm focuses on the balance of diversity and convergence by making use of the uncertainty information in the approximated objective values given by the Kriging models, the distr…