Realizability of concurrent recursive programs

We study the realizability problem for concurrent recursive programs: given a distributed system architecture and a sequential specification over words, find a distributed automata implementation that is equivalent to the specification. This problem is well-studied as far as finite-state processes are concerned, and it has a solution in terms of Zielonka’s Theorem. We lift Zielonka’s Theorem to the case where processes are recursive and modeled as visibly pushdown (or, equivalently, nested-word) automata. However, contrarily to the finite-state case, it is undecidable whether a specification is realizable or not. Therefore, we also consider suitable underapproximation techniques from the literature developed for multi-pushdown systems, and we show that they lead to a realizability framework with effective algorithms.     

The effect of externally applied uniaxial compressive stress on the magnetic properties of power MnZn-ferrites

In this article, the effect of uniaxial compressive stress with magnitude up to ~53 MPa on the magnetic properties of power MnZn-ferrites of the general chemical formula Mn_0.81Zn_0.19Fe_2+δΟ₄ has been investigated. In addition, the effects of various process operational or material structural parameters such as sintering partial pressure of oxygen, final density and grain size on the stress sensitivity of the polycrystalline component have been studied. As found, the temperature of the secondary initial permeability maximum and the power loss minimum are reduced by ~0.5 °C MPa^?1 while the magnetic flux density by ~1.4 mT MPa^?1 upon the exertion of uniaxial compressive stress on the component. The sintering partial pressure of oxygen exhibits an optimum at which the material stress sensitivity becomes minimum. The results have been explained on the basis of variations induced in the anisotropy field as well as on the basis of the defect structure of the material and its dependency on the partial pressure of oxygen.     

MSM Actuators: Design Rules and Control Strategies

Magnetic shape memory (MSM) alloys are comparatively new active materials which can be used for several industrial applications, ranging from precise positioning systems to advanced robotics. Beyond the material research, which deals with the basic thermo‐magneto‐mechanical properties of the crystals, the design as well as the control of the actuators displacement is an essential challenge. This paper addresses those two topics, trying to give to the reader a useful overview of existing results, but also presents new ideas. First, it introduces and discusses in details some possible designs, with a special emphasis on innovative actuator design concepts which are able to exploit the particular potentialities of MSM elements. The second focus of the paper is on the problem of designing a controller, i.e., an algorithm that allows to obtain a required performance from the actuator. The proposed control strategies try to take into account two main characteristics of MSM elements: the hysteresis and the temperature dependence. The effectiveness of the strategies is emphasized by experimental results performed on a commercially available MSM actuator demonstrator.     

Using machine learning to predict the density profiles of surface-densified wood based on cross-sectional images

Over the past decades, the surface densification of solid wood has received increased attention. However, the inhomogeneous density distribution in the densification direction might be a challenge with regard to process control within a large-scale production process, as the density profile governs many relevant properties of surface-densified wood. Currently, the measurement of density profiles relies on sensitive X-ray equipment and is difficult to integrate into an on-line process. Hence, in this study, three machine learning approaches were applied to predict the density profiles of surface-densified Scots pine specimens, only based on visual image acquisition—a technology that is ubiquitous in the wood industry: partial least squares (PLS) regression, artificial neural networks (ANN), and convolutional neural networks (CNN). The machine learning models were trained on images of the specimen cross-sections as input data, and X-ray density profiles as output data. There were 1850 observations, and the model performance was evaluated on external test sets. The models had mean absolute percentage errors of the predicted values between 9 and 18%; the CNN achieving the smallest error (9.24%). A deeper analysis of the data revealed that the ANN approach performed inconsistently between observations. PLS regression predicted the main density peak to a high accuracy but could not model other features. Only the CNN could reliably model the main density peak, wide growth rings, and the important region between the specimen surface and the main density peak. The ability of the models to generalise to untypical new data was improved by augmentation of the training data.

     

Merging project planning and Web enabled dynamic workflowtechnologies

The MILOS system supports dynamic coordination of distributed software development teams by integrating project planning and workflow technologies over the Internet. The three-tiered Java architecture enables plan refinements to be made on the fly, and a change management component automatically creates traceability relationships between project entities     

Unusual Phase Formation in Reactively Sputter-Deposited La—Co—O Thin-Film Libraries

La-based perovskites are versatile materials that are of interest for solid oxide fuel cells and electrocatalytic water splitting. During fabrication of composition spread thin-film libraries of La—Co-based oxide systems, an unusual phase formation phenomenon is observed: instead of the expected continuous composition gradient, single-phase regions with homogeneous composition form (La₂O₃ or stoichiometric La-perovskite). This phenomenon, which occurs during reactive cosputtering, is independent of the used substrate. However, a dependency on the O₂-portion in the process gas and the substrate temperature is observed. It can be described as a self-organized growth, where excess transition metal cannot be incorporated into the lattices of the forming single-phase regions, and therefore, not into the growing film. It is hypothesized that due to the high reactivity of La and the significantly low formation energies of La₂O₃ and La-perovskites, the reactive sputter deposition of La-based oxide films, which is a physical vapor deposition process, can turn partially—regarding film growth—into a chemical vapor deposition-like process. The described single-phase regions form and lead to a discontinuous composition spread, with preferred growth of the thermodynamically most stable phases. This phenomenon can be leveraged for the exploration of multinary perovskite thin-film libraries, where the B-site atoms of La-perovskites are systematically substituted.