The dynamic behaviour of rigid blocks subjected to support excitation is represented by discontinuous differential equations with state jumps. In the numerical simulation of these systems, the jump times corresponding to the numerical trajectory do not coincide with the ones of the given problem. When multiple state jumps occur, this approximation may affect the accuracy of the solution and even cause an order reduction in the method. Focus here is on the error behaviour in the numerical dynamic.

The integral representation of some biological phenomena consists in Volterra equations whose kernels involve a convolution term plus a non convolution one. Some significative applications arise in linearised models of cell migration and collective motion, as described in Di Costanzo et al. (Discrete Contin. Dyn. Syst. Ser. B 25 (2020) 443-472), Etchegaray et al. (Integral Methods in Science and Engineering (2015)), Grec et al. (J. Theor. Biol. 452 (2018) 35-46) where the asymptotic behaviour of the analytical solution has been extensively investigated.

Trust and reputation systems are decision support tools used to drive parties' interactions on the basis of parties' reputation. In such systems, parties rate with each other after each interaction.

Reputation systems are nowadays widely used to support decision making in networked systems. Parties in such systems rate each other and use shared ratings to compute reputation scores that drive their interactions. The existence of reputation systems with remarkable differences calls for formal approaches to their analysis. We present a verification methodology for reputation systems that is based on the use of the coordination language Klaim and related analysis tools.

Parties of reputation systems rate each other and use ratings to compute reputation scores that drive their interactions. When deciding which reputation model to deploy in a network environment, it is important to find the most suitable model and to determine its right initial configuration. This calls for an engineering approach for describing, implementing and evaluating reputation systems while taking into account specific aspects of both the reputation systems and the networked environment where they will run.

The popularity of the cloud computing paradigm is opening new opportunities for collaborative computing. In this paper we tackle a fundamental problem in open-ended cloud-based distributed computing platforms, i.e., the quest for potential collaborators. We assume that cloud participants are willing to share their computational resources for shared distributed computing problems, but they are not willing to disclose the details of their resources. Lacking such information, we advocate to rely on reputation scores obtained by evaluating the interactions among participants.

Social Web Services (SWSs) constitute a novel paradigm of service-oriented computing, where Web services, just like humans, sign up in social networks that guarantee, e.g., better service discovery for users and faster replacement in case of service failures. In past work, composition of SWSs was mainly supported by specialised social networks of competitor services and cooperating ones. In this work, we continue this line of research, by proposing a novel SWSs composition procedure driven by the SWSs reputation.

Earth Observation (EO) mining systems aim at supporting efficient access and exploration of large volumes of image products. In this work, we address the problem of content-based image retrieval via example-based queries from Petabyte-scale EO data archives. To this end, we propose an interactive data mining system that relies on distributing unsupervised ingestion processes onto virtual machine instances in elastic, on-demand computing infrastructures that also support archive-scale content indexing via a "big data" analytics cluster-computing framework.

Results from direct numerical simulations (DNS) of particle relative dispersion in three-dimensional homogeneous and isotropic turbulence at Reynolds number Re?~300 are presented. We study point-like passive tracers and heavy particles, at Stokes number St=0.6,1 and 5. Particles are emitted from localised sources, in bunches of thousands, periodically in time, allowing an unprecedented statistical accuracy to be reached, with a total number of events for two-point observables of the order of 1011.