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Well-definedness and semantic type-checking for the nested relational calculus

The well-definedness problem for a programming language consists of checking, given an expression and an input type, whether the semantics of the expression is defined for all inputs adhering to the input type. A related problem is the semantic type-checking problem which consists of checking, given an expression, an input type, and an output type whether the expression always returns outputs adhering to the output type on inputs adhering to the input type. Both problems are undecidable for general-purpose programming languages. In this paper we study these problems for the Nested Relational Calculus, a specific-purpose database query language. We also investigate how these problems behave in the presence of programming language features such as singleton coercion and type tests.     

Steam bubble condensation in sub-cooled water in case of co-current vertical pipe flow

The structure of a steam-water flow in a vertical pipe of 195.3 mm inner diameter was studied using novel wire-mesh sensors for high-pressure/high-temperature operation (max 7 MPa/286 °C). Tests were carried out at pressures of 1 and 2 MPa under nearly adiabatic conditions as well as with slightly sub-cooled water (6 K at max). Steam was injected into sub-cooled water and condensed during the upwards flow. The evolution of radial gas fraction profiles and bubble size distributions along the pipe in a high-pressure steam-water flow was measured for the first time. The experimental data allow correlating the intensity of steam condensation in contact with sub-cooled water with the structure of the interfacial area and the bubble size distribution, which is very important for the model development. The data were used to test the complex interaction of local bubble distributions, bubble size distributions and local heat and mass transfer. The model considers a large number of bubble classes (50). This allows the investigation of the influence of the bubble size distribution. The results of the simulations show a good agreement with the experimental data. The condensation process is clearly slower, if the injection nozzle diameter is increased (from 1 to 4 mm orifices). Also bubble break-up has a strong influence on the condensation process because of the change of the interfacial area. Some modelling errors arise from the uncertainty of the interfacial area for large bubbles and the heat transfer coefficient.     

Experimental Modelling using Low Melting Point Metallic Melts: Relevance for Metallurgical Engineering

This paper describes model experiments with liquid metals of low melting point as an important tool to investigate the flow structure and related transport processes in melt flows being relevant for metallurgical applications. Three examples have been selected to demonstrate the benefit of such experiments, namely the electromagnetic stirring of a metallic melt in a pool, the directional solidification of Al‐Si alloys under the influence of an electromagnetically driven flow and the behaviour of a liquid metal bubble plume if a vertically travelling field is applied. Results of flow measurements are presented and the relevance for metallurgical processes is discussed.     

A Compact Model for Valence-Band Electron Tunneling Current in Partially Depleted SOI MOSFETs

The valence-band electron (EVB) tunneling current in partially depleted silicon-on-insulator (SOI) MOSFETs increases as the gate oxide gets thinner and affects the dynamic behavior of devices and circuits. We present an engineering model of EVB tunneling current based on the surface-potential formulation. The new model is implemented in a SOI MOSFET compact model and is used to study the impact of EVB tunneling on circuit performance. Simulations of stacked logic gates show that the EVB tunneling current not only boosts circuit switching speed but also mitigates the history dependence of propagation delays     

Systemic aspects of R&D policy subsidies for R&D collaborations and their effects on private R&D

This paper analyses how context- and time-dependent factors determine the impact of R&D subsidies on firm behaviour with respect to private R&D expenditures. Based on German R&D survey data, we combine propensity score matching with a difference-in-difference estimator in order to measure the causal influence of public direct R&D project funding on firm behaviour. Our results indicate that (i) repeated participation in R&D projects on average leads to a higher increase in R&D expenditures than once-off funding; (ii) the aggregate effect of R&D funding on R&D expenditures of business firms is somewhat higher for business–business collaboration projects than for science–business collaboration projects; (iii) R&D expenditures of business firms that cooperate with science show a higher share of external R&D spending. Results of one particular cluster programme indicate that at least the short-term development of R&D does not so much depend on which programme direct R&D project funding is applied to.     

Fast Image‐Based Modeling of Astronomical Nebulae

Astronomical nebulae are among the most complex and visually appealing phenomena known outside the bounds of the Solar System. However, our fixed vantage point on Earth limits us to a single known view of these objects, and their intricate volumetric structure cannot be recovered directly. Recent approaches to reconstructing a volumetric 3D model use the approximate symmetry inherent to many nebulae, but require several hours of computation time on large multi‐GPU clusters. We present a novel reconstruction algorithm based on group sparsity that reaches or even exceeds the quality of prior results while taking only a fraction of the time on a conventional desktop PC, thereby enabling end users in planetariums or educational facilities to produce high‐quality content without expensive hardware or manual modeling. In principle, our approach can be generalized to other transparent phenomena with arbitrary types of user‐specified symmetries.     

Application of probabilistic and fuzzy cognitive approaches in semantic web framework for medical decision support

This study aimed to focus on medical knowledge representation and reasoning using the probabilistic and fuzzy influence processes, implemented in the semantic web, for decision support tasks. Bayesian belief networks (BBNs) and fuzzy cognitive maps (FCMs), as dynamic influence graphs, were applied to handle the task of medical knowledge formalization for decision support. In order to perform reasoning on these knowledge models, a general purpose reasoning engine, EYE, with the necessary plug-ins was developed in the semantic web. The two formal approaches constitute the proposed decision support system (DSS) aiming to recognize the appropriate guidelines of a medical problem, and to propose easily understandable course of actions to guide the practitioners. The urinary tract infection (UTI) problem was selected as the proof-of-concept example to examine the proposed formalization techniques implemented in the semantic web. The medical guidelines for UTI treatment were formalized into BBN and FCM knowledge models. To assess the formal models’ performance, 55 patient cases were extracted from a database and analyzed. The results showed that the suggested approaches formalized medical knowledge efficiently in the semantic web, and gave a front-end decision on antibiotics’ suggestion for UTI.     

Regularization of non-homogeneous dynamic Bayesian networks with global information-coupling based on hierarchical Bayesian models

To relax the homogeneity assumption of classical dynamic Bayesian networks (DBNs), various recent studies have combined DBNs with multiple changepoint processes. The underlying assumption is that the parameters associated with time series segments delimited by multiple changepoints are a priori independent. Under weak regularity conditions, the parameters can be integrated out in the likelihood, leading to a closed-form expression of the marginal likelihood. However, the assumption of prior independence is unrealistic in many real-world applications, where the segment-specific regulatory relationships among the interdependent quantities tend to undergo gradual evolutionary adaptations. We therefore propose a Bayesian coupling scheme to introduce systematic information sharing among the segment-specific interaction parameters. We investigate the effect this model improvement has on the network reconstruction accuracy in a reverse engineering context, where the objective is to learn the structure of a gene regulatory network from temporal gene expression profiles. The objective of the present paper is to expand and improve an earlier conference paper in six important aspects. Firstly, we offer a more comprehensive and self-contained exposition of the methodology. Secondly, we extend the model by introducing an extra layer to the model hierarchy, which allows for information-sharing among the network nodes, and we compare various coupling schemes for the noise variance hyperparameters. Thirdly, we introduce a novel collapsed Gibbs sampling step, which replaces a less efficient uncollapsed Gibbs sampling step of the original MCMC algorithm. Fourthly, we show how collapsing and blocking techniques can be used for developing a novel advanced MCMC algorithm with significantly improved convergence and mixing. Fifthly, we systematically investigate the influence of the (hyper-)hyperparameters of the proposed model. Sixthly, we empirically compare the proposed global information coupling scheme with an alternative paradigm based on sequential information sharing.