In today’s data-driven world, people are increasingly prioritising data privacy and control. This awareness has sparked an initiative for a decentralized web, where web applications no longer rely on centralized data storage. Solid, a prominent approach for a decentralized web, allows users to store their data in decentralized pods of their control. However, the integration of data from various and potentially untrusted sources can lead to malicious or harmful results and impair user experience. To solve this problem for social networks, we propose a trust-aware and decentralized web application called TrADS. It utilises the MVC pattern to integrate external data from Solid pods based on trust evaluations. In this article, we extend our first paper on TrADS with further details on related work assessment, concept and implementation. We are also extending our evaluation to a second international user study with another 64 participants. We measure the user experience instead of pure usability and form two separate groups of participants, one of which experiences the trust awareness of TrADS and one of which does not. The results do not yet show significant improvement in the user experience but show that after using trust awareness in a social network, users favour a network with such features.

Particle simulations are popular methods in computational science in areas, such as biology, chemistry, or astrophysics, in which the interaction of particles plays a major role. Efficient implementations of particle simulations often exploit a separation of computations by a cutoff radius. The pairwise interactions between particles are then computed only within the cutoff radius while interactions beyond the cutoff radius might be neglected or computed approximately. Thus, the efficient implementation of the calculations within the cutoff radius can be crucial for an overall efficiency. In this article, we study these computationally intensive inner cutoff radius calculations and propose a portable parallel implementation on a hybrid CPU/GPU architecture. Especially, we have chosen particle methods from the ScaFaCoS library and have implemented a parallel version of the cutoff-based algorithm in OpenCL. Performance results are shown for two different solvers of the ScaFaCoS library. The influences of the particle distribution and the utilized hardware are investigated.