Abstract
This paper presents a new solution algorithm to solve the resource-constrained project scheduling problem with activity splitting and setup times. The option of splitting activities, known as activity preemption, has been studied in literature from various angles, and an overview of the main contributions will be given. The solution algorithm makes use of a meta-heuristic search for the resource-constrained project scheduling problem (RCPSP) using network transformations to split activities in subparts. More precisely, the project network is split up such that all possible preemptive parts are incorporated into an extended network as so-called activity segments, and setup times are incorporated between the different activity segments. Due to the inherent complexity to solve the problem for such huge project networks, a solution approach is proposed that selects the appropriate activity segments and ignores the remaining segments using a boolean satisfiability problem solver, and afterwards schedules these projects to near-optimality with the renewable resource constraints. The algorithm has been tested using a large computational experiment with five types of setup times. Moreover, an extension to the problem with overlaps between preemptive parts of activities has been proposed and it is shown that our algorithm can easily cope with this extension without changing it. Computational experiments show that activity preemption sometimes leads to makespan reductions without requiring a lot of splits in the activities. Moreover, is shown that the degree of these makespan reductions depends on the network and resource indicators of the project instance.

Abstract
In this work we investigate which aspects of data protection regulation must be carefully observed when implementing Blockchain-based projects in smart cities. This technology provides interesting properties and allows governments to develop flexible and innovative data management systems. Nevertheless, realizing the benefits of using Blockchains requires understanding the government processes along with the legal framework and political setting imposed on government. Though it is a buzzword, Blockchain may not always be the best solution for data processing, and carrying out a Data Protection Impact Assessment could allow an analysis of the necessity and proportionality of the mechanism. Furthermore, principles relating to security of data remain applicable to Blockchains. We discuss points of interaction between Blockchain technology and the European Union data protection framework, and provide recommendations on how to better develop Blockchain-based projects in smart cities. The findings of the study should provide public sector actors with a guideline to assess the real necessity and better format of a Blockchain-based application.

Abstract
Novel data collection tools, methods and new techniques in biotechnology can facilitate improved health strategies that are customised to each individual. One key challenge to achieve this is to take advantage of the massive volumes of personal anonymous data, relating each profile to health and disease, while accounting for high diversity in individuals, populations and environments. These data must be analysed in unison to achieve statistical power, but presently cohort data repositories are scattered, hard to search and integrate, and data protection and governance rules discourage central pooling.

Abstract
A femtosecond laser direct writing system was developed to explore the fabrication of periodic structures in optical fibers. The possibility to write type I first- and second-order Bragg gratings in the same single-mode fiber (SMF-28e), with reflectivities of 99.6 % and 59.3 %, respectively, is presented. The fabrication of structures (waveguides and grating) in a coreless and in a SMF-28e fiber was first demonstrated, and the gratings were then exposed to a thermal annealing up to 1000 degrees C. The FBG inscribed in the SMF-28e fiber presents thermal stability at temperatures of 800 degrees C and a temperature sensitivity of 14.34 pm/degrees C was determined.

Abstract
This paper describes a novel framework for real-time sonification of surface textures in virtual reality (VR), aimed towards realistically representing the experience of driving over a virtual surface. A combination of capturing techniques of real-world surfaces are used for mapping 3D geometry, texture maps or auditory attributes (aural and vibrotactile) feedback. For the sonification rendering, we propose the use of information from primarily graphical texture features, to define target units in concatenative sound synthesis. To foster models that go beyond current generation of simple sound textures (e.g., wind, rain, fire), towards highly “synchronized” and expressive scenarios, our contribution draws a framework for higher-level modeling of a bicycle's kinematic rolling on ground contact, with enhanced perceptual symbiosis between auditory, visual and vibrotactile stimuli. We scanned two surfaces represented as texture maps, consisting of different features, morphology and matching navigation. We define target trajectories in a 2-dimensional audio feature space, according to a temporal model and morphological attributes of the surfaces. This synthesis method serves two purposes: a real-time auditory feedback, and vibrotactile feedback induced through playing back the concatenated sound samples using a vibrotactile inducer speaker. Copyright: © 2019 Eduardo Magalhães et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract
The continuous real-time motor interaction with our environment requires the capacity to measure and produce time intervals in a highly flexible manner. Recent neurophysiological evidence suggests that the neural computational principles supporting this capacity may be understood from a dynamical systems perspective: Inputs and initial conditions determine how a recurrent neural network evolves from a "resting state" to a state triggering the action. Here we test this hypothesis in a time measurement and time reproduction experiment using a model of a robust neural integrator based on the theoretical framework of dynamic neural fields. During measurement, the temporal accumulation of input leads to the evolution of a self-stabilized bump whose amplitude reflects elapsed time. During production, the stored information is used to reproduce on a trial-by-trial basis the time interval either by adjusting input strength or initial condition of the integrator. We discuss the impact of the results on our goal to endow autonomous robots with a human-like temporal cognition capacity for natural human-robot interactions.