The effect of hydrodynamic conditions on the detection of aqueous solutions of toluene by means of an inverted graded-index fiber

This paper deals with the effect of hydrodynamic conditions on the detection of toluene dissolved in water by means of fibers with a truncated parabolic inverted-graded index (IGI) profile. The detection is based on refractive-index changes of a detection layer of polydimethylsiloxane (PDMS) applied on the fiber which are induced by penetration of toluene into the layer. The dependence of the response of the sensing fiber placed in a flow cell on the flow velocity of the detected aqueous solution of toluene has been investigated. The sensing fiber was successively exposed to flows of distilled water and aqueous solutions of toluene. The fiber responses were measured for three chosen concentrations of the toluene solutions and for five flow velocities of the solutions. From these results the dependence of the relative decrease of the output signal on the flow velocity was determined. It has been found that the magnitude of output signal changes is approximately proportional to the solution concentration and the rate of the signal changes in the tested concentration range depends approximately linearly on the solution flow velocity.     

Digitalisation and employment in manufacturing

The present work tackles the issue of the effects of digitalisation on employment. This issue has been attracting a growing interest, in particular because of the anxiety generated by the idea that digital technologies could cancel a large number of jobs. Although I agree with argument put forward in opposition to the existence of a causal link between technological innovation and increased productivity at the macroeconomic level, I believe that the novelty and pervasiveness of digital technologies require more in-depth micro-level analysis in order to understand the extent to which new digital technologies are currently employed by leading manufacturing companies and the ways new technologies are affecting employment. The empirical findings show that among the different technologies included under the umbrella of Industry 4.0, mainly robots have received a great deal of attention so far, while the current application and employment impact for other emerging technological opportunities such as 3D printing, Internet of Things, Augmented reality, Big data Analytics have not been studied yet. In relation to the qualitative changes of the labour market, our empirical research confirms that there are new types of skills that will be demanded in the future in manufacturing, in particular in relation to service provision and software development.     

CYCLON: Inexpensive Membership Management for Unstructured P2P Overlays

Unstructured overlays form an important class of peer-to-peer networks, notably when content-based searching is at stake. The construction of these overlays, which is essentially a membership management issue, is crucial. Ideally, the resulting overlays should have low diameter and be resilient to massive node failures, which are both characteristic properties of random graphs. In addition, they should be able to deal with a high node churn (i.e., expect high-frequency membership changes). Inexpensive membership management while retaining random-graph properties is therefore important. In this paper, we describe a novel gossip-based membership management protocol that meets these requirements. Our protocol is shown to construct graphs that have low diameter, low clustering, highly symmetric node degrees, and that are highly resilient to massive node failures. Moreover, we show that the protocol is highly reactive to restoring randomness when a large number of nodes fail.Spyros Voulgaris is a PhD student in the Computer Systems department at the Vrije Universiteit Amsterdam. He received his MSc degree from the University of Michigan, Ann Arbor, and his BSc degree from the University of Patras, Greece. His research involves peer-to-peer systems, epidemic protocols, and ad-hoc networks. He is a scholarship recipient of the Greek State Scholarships Foundation (IKY) and the Alexander Onassis Foundation.Daniela Gavidia is a PhD student in the Computer Systems group at the Vrije Universiteit Amsterdam. She received her MSc degree from the Universiteit van Amsterdam. Her research interests include peer-to-peer systems and ad-hoc networks. Her recent work focuses on information dissemination in ad-hoc environments.Maarten van Steen is professor of Computer Science at the Vrije Universiteit Amsterdam. His research concentrates on large-scale distributed systems, notably content delivery networks and peer-to-peer systems. He is senior member of the IEEE and member of the ACM.     

A Python extension for the massively parallel multiphysics simulation framework waLBerla

We present a Python extension to the massively parallel HPC simulation toolkit waLBerla. waLBerla is a framework for stencil based algorithms operating on block-structured grids, with the main application field being fluid simulations in complex geometries using the lattice Boltzmann method. Careful performance engineering results in excellent node performance and good scalability to over 400,000 cores. To increase the usability and flexibility of the framework, a Python interface was developed. Python extensions are used at all stages of the simulation pipeline: they simplify and automate scenario setup, evaluation, and plotting. We show how our Python interface outperforms the existing text-file-based configuration mechanism, providing features like automatic nondimensionalization of physical quantities and handling of complex parameter dependencies. Furthermore, Python is used to process and evaluate results while the simulation is running, leading to smaller output files and the possibility to adjust parameters dependent on the current simulation state. C++ data structures are exported such that a seamless interfacing to other numerical Python libraries is possible. The expressive power of Python and the performance of C++ make development of efficient code with low time effort possible.     

Network regression with predictive clustering trees

Network data describe entities represented by nodes, which may be connected with (related to) each other by edges. Many network datasets are characterized by a form of autocorrelation, where the value of a variable at a given node depends on the values of variables at the nodes it is connected with. This phenomenon is a direct violation of the assumption that data are independently and identically distributed. At the same time, it offers an unique opportunity to improve the performance of predictive models on network data, as inferences about one entity can be used to improve inferences about related entities. Regression inference in network data is a challenging task. While many approaches for network classification exist, there are very few approaches for network regression. In this paper, we propose a data mining algorithm, called NCLUS, that explicitly considers autocorrelation when building regression models from network data. The algorithm is based on the concept of predictive clustering trees (PCTs) that can be used for clustering, prediction and multi-target prediction, including multi-target regression and multi-target classification. We evaluate our approach on several real world problems of network regression, coming from the areas of social and spatial networks. Empirical results show that our algorithm performs better than PCTs learned by completely disregarding network information, as well as PCTs that are tailored for spatial data, but do not take autocorrelation into account, and a variety of other existing approaches.     

Optimal control of advertising and production of a seasonal product with nondifferentiable demand

We consider an application of optimal control theory to a marketing problem, in which a firm seeks the maximum profit by producing and selling a seasonal product. We assume that the firm can advertise its product in order to improve the firm goodwill and affect the product demand positively. In particular the demand is zero when the goodwill is less than a fixed threshold. The original optimal control problem is shown to be equivalent to a nonlinear programming problem, which has an optimal solution, possibly not unique. Editor: G. Leitmann     

An iterative algorithm for simulation error based identification of polynomial input–output models using multi-step prediction

Effective identification of polynomial input–output models for applications requiring long-range prediction or simulation performance relies on both careful model selection and accurate parameter estimation. The simulation error minimisation (SEM) approach has been shown to provide significant advantages in the model selection phase by ruling out candidate models with good short-term prediction capabilities but unsuitable long-term dynamics. However, SEM-based parameter estimation has been generally avoided due to excessive computational effort. This article extends to the nonlinear case a computationally efficient approach for this task, that was previously developed for linear models, based on the iterative estimation of predictors with increasing prediction horizon. Conditions for the applicability of the approach to various model classes are also discussed. Finally, some examples are provided to show the effectiveness and computational convenience of the proposed algorithm for polynomial input–output identification, as well as the improvements achievable by enforcing SEM parameter estimation. A benchmark for nonlinear identification is also analysed, with encouraging results.     

Performance analysis of parallel Schwarz preconditioners in the LES of turbulent channel flows

We present a comparative study of parallel Schwarz preconditioners in the solution of linear systems arising in a Large Eddy Simulation (LES) procedure for turbulent plane channel flows. This procedure applies a time-splitting technique to suitably filtered Navier–Stokes equations, in order to decouple the continuity and momentum equations, and uses a semi-implicit scheme for time integration and finite volumes for space discretisation. This approach requires the solution of four sparse linear systems at each time step, accounting for a large part of the overall simulation; hence the linear system solvers are a crucial component in the whole procedure. Several preconditioners are applied in the simulation of a reference test case for the LES community, using discretisation grids of different sizes, with the aim of analysing the effects of different algorithmic choices defining the preconditioners, and identifying the most effective ones for the selected problem. The preconditioners, coupled with the GMRES method, are run within SParC-LES, a recently developed LES code based on the PSBLAS and MLD2P4 libraries for parallel sparse matrix computations and preconditioning.     

Necessary conditions for positive realizability of continuous-time linear systems

This paper deals with the positive realization problem. The problem is to find, from a given transfer function, a state equation in which state variables and the output take nonnegative values whenever initial states and inputs are nonnegative. Necessary conditions are investigated and a new one is given, together with some related results.