Happiness and impulse buying: An exploration into the perceptions of female consumers aged between 18 and 35 in Germany

Impulse behaviour in general and impulse buying in particular have a long history of negative associations in research. Consumers are advised by the popular press to refrain from impulse buying. Marketing practitioners, on the other hand, strive to further increase consumer impulse buying expenditures, which have already been on the increase for decades. This may be an indication that impulse buying makes consumers feel happy. Although the topic happiness has received considerable attention in various fields of research, there is little evidence of an in-depth empirical exploration of the role of happiness in impulse buying, which was addressed by this study. This thesis was based on the phenomenological paradigm and adopted a subjective stance, exploring happiness in female consumers' impulse buying experiences. In this inductive exploratory study, qualitative data were collected from focus groups and individual interviews with female consumers aged between 18 and 35 years in Germany. This research sought to investigate how happiness evolves over the impulse buying experience, which was addressed by the longitudinal nature of collecting data over a period of three months in weekly individual interviews. The empirical evidence showed that the pursuit of happiness is one of the major motivations for impulse buying and the subsequent evaluation of the purchase. For instance, the presentation of a newly acquired item to other people with the intention of receiving positive feedback is one of the eight themes which emerged from the iterative process of data analysis. The findings indicate that impulse buying is often appreciated by consumers as an enjoyable experience which may yield positive emotions even after careful reflection some time after the purchase. Impulse buying should not generally be devalued as the dark side of consumption. This research underlines the complexity of impulse buying and indicates overlaps and interdependencies with planned buying. Suggestions for marketing practitioners and retail managers on how to increase impulse buying activities are implicit in these findings

Robust Reference-Free Sim-to-Real Reinforcement Learning for Bipedal Locomotion

In recent years, model-free Deep Reinforcement Learning (RL) has become an increasingly popular alternative to more traditional model-based or optimization-based control methods in solving robotic legged locomotion. However, deploying RL in the real world can be a significant undertaking. Constructing reward functions which compel controllers to learn the desired behavior is not straightforward. For example, can a reward function which trains a controller to stand be easily modified to train it to walk or run? This thesis seeks to provide insights into training such controllers in ways that make desired behaviors easier to realize by parameterizing behaviors in a low-dimensional periodic space which covers the entire breadth of legged gaits. In addition, it explores the limits of the approach by training controllers to interact (in the real world) with challenging terrain conventionally assumed to be extremely difficult for bipedal robots

Recurrent Neural Networks for Robotic Control of a Human-Scale Bipedal Robot

Dynamic bipedal locomotion is among the most difficult and yet relevant problems in modern robotics. While a multitude of classical control methods for bipedal locomotion exist, they are often brittle or limited in capability. In recent years, work in applying reinforcement learning to robotics has lead to superior performance across a range of tasks, including bipedal locomotion. However, crafting a real-world controller using reinforcement learning is a difficult task, and the majority of successful efforts use simple feedforward neural networks to learn a control policy. Though these successful efforts have demonstrated learned control of a Cassie robot, the wide range of behaviors that can be learned by a memory-based architecture, which include walking at varying step frequencies, sidestepping, and walking backwards, (all in the same learned controller) have not yet been demonstrated. In keeping with recent work which has shown that memory-based architectures have resulted in better performance on a variety of reinforcement learning problems, I demonstrate some advantages of using a sophisticated, memory-based neural network architecture paired with state of the art reinforcement learning algorithms to achieve highly robust control policies on Agility Robotics' bipedal robot, Cassie. I also visualize the internal learned behavior of the memory using principal component analysis, and show that the architecture learns highly cyclic behaviors. I show that dynamics randomization is a key tool in training robust memory-based neural networks, and that these networks can sometimes fail to transfer to hardware if not trained with dynamics randomization. I also demonstrate that various parameters of the dynamics, such as ground friction or ground slope angle, can be reconstructed by examining the internal memory of a recurrent neural network. This opens the door to automatic disturbance observers or online system-ID, which would be of significant benefit to problems which require hand-written disturbance observers requiring manual tuning

Muscle of obese insulin-resistant humans exhibits losses in proteostasis and attenuated proteome dynamics that are improved by exercise training

We examined muscle proteostasis in obese insulin-resistant (OIR) individuals to determine whether endurance exercise could positively influence proteome dynamics in this population. Male OIR (n = 3) and lean, healthy controls (LHC; n = 4) were recruited and underwent a 14-d measurement protocol of daily deuterium oxide (D2O) consumption and serial biopsies of vastus lateralis muscle. The OIR group then completed 10-weeks of high-intensity interval training (HIIT), encompassing 3 sessions per week of cycle ergometer exercise with 1 min intervals at 100 % maximum aerobic power (Wmax) interspersed by 1 min recovery periods. The number of intervals per session progressed from 4 to 8, and during weeks 8-10 the 14-d measurement protocol was repeated. The abundance and turnover rates of 880 and 301 proteins, respectively, were measured. OIR and LHC muscle exhibited 352 differences (p < 0.05, false discovery rate (p < 0.05) differences in protein turnover. OIR muscle was enriched with markers of metabolic stress, protein misfolding and components of the ubiquitin-proteasome system, and the turnover rate of many of these proteins was less compared to LHC muscle. HIIT altered the abundance of 53 proteins and increased the turnover rate of 22 proteins (p < 0.05) in OIR muscle and tended to restore proteostasis, evidenced by increasing muscle protein turnover rates and normalizing proteasome composition in OIR participants. In conclusion, obesity and insulin resistance are associated with compromised muscle proteostasis, which can be partially restored by endurance exercise

High Deposition Rate Aluminium Doped Zinc Oxide Films with Highly Efficient Light Trapping for Silicon Thin Film Solar Cells

Abstract Aluminium doped zinc oxide films were deposited on glass substrates at high rates by reactive mid frequency sputtering. The in-line sputter system allows oxygen influx along the middle and sides of a dual cathode system. The effect of varying the oxygen flow from the sides on the electrical and optical properties together with the surface morphology after wet chemical etching was investigated. Increasing the amount of oxygen flow from the sides improved the resistivity profile of static prints and gave highly conductive and transparent films in dynamic deposition mode. The etched films developed rough surface textures with effective light scattering which could be controlled by the oxygen balance between the middle and sides. Optimally textured films were used as front contacts in 1cm2 single junction µc-Si:H solar cells yielding an initial efficiency of 8.4 %. The improvement in light trapping lead to short circuit densities higher than that of the reference solar cells

Fighting Enemies and Noise: Competition of Residents and Invaders in a Stochastically Fluctuating Environment

The possible control of competitive invasion by infection of the invader and multiplicative noise is studied. The basic model is the Lotka-Volterra competition system with emergent carrying capacities. Several stationary solutions of the non-infected and infected system are identified as well as parameter ranges of bistability. The latter are used for the numerical study of invasion phenomena. The diffusivities, the infection but in particular the white and coloured multiplicative noise are the control parameters. It is shown that not only competition, possible infection and mobilities are important drivers of the invasive dynamics but also the noise and especially its color and the functional response of populations to the emergence of noise

Secondary organic aerosol formation from isoprene photooxidation during cloud condensation-evaporation cycles

Abstract. The impact of cloud events on isoprene secondary organic aerosol (SOA) formation has been studied from an isoprene ∕ NOx ∕ light system in an atmospheric simulation chamber. It was shown that the presence of a liquid water cloud leads to a faster and higher SOA formation than under dry conditions. When a cloud is generated early in the photooxidation reaction, before any SOA formation has occurred, a fast SOA formation is observed with mass yields ranging from 0.002 to 0.004. These yields are 2 and 4 times higher than those observed under dry conditions. When the cloud is generated at a later photooxidation stage, after isoprene SOA is stabilized at its maximum mass concentration, a rapid increase (by a factor of 2 or higher) of the SOA mass concentration is observed. The SOA chemical composition is influenced by cloud generation: the additional SOA formed during cloud events is composed of both organics and nitrate containing species. This SOA formation can be linked to the dissolution of water soluble volatile organic compounds (VOCs) in the aqueous phase and to further aqueous phase reactions. Cloud-induced SOA formation is experimentally demonstrated in this study, thus highlighting the importance of aqueous multiphase systems in atmospheric SOA formation estimations. The authors thank Arnaud Allanic, Sylvain Ravier, Pascal Renard and Pascal Zapf for their contributions in the experiments. The authors also acknowledge the institutions that have provided financial support: the French National Institute for Geophysical Research (CNRS-INSU) within the LEFE-CHAT program through the project “Impact de la chimie des nuages sur la formation d’aérosols organiques secondaires dans l’atmosphère” and the French National Agency for Research (ANR) project CUMULUS ANR-2010-BLAN-617-01. This work was also supported by the EC within the I3 project “Integrating of European Simulation Chambers for Investigating Atmospheric Processes” (EUROCHAMP-2, contract no. 228335). The authors thank the MASSALYA instrumental platform (Aix Marseille Université, lce.univ-amu.fr) for the analysis and measurements used in this paper.This is the final version of the article. It first appeared from Copernicus Publications via http://dx.doi.org/10.5194/acp-16-1747-201

Preface. Bifurcations and Pattern Formation in Biological Applications

In the preface we present a short overview of articles included in the issue "Bifurcations and pattern formation in biological applications" of the journal Mathematical Modelling of Natural Phenomena