Diterpene structure elucidation from 13cnmr spectra with inductive logic programming

We present a novel application ofInductive Logic Programming (ILP) to the problem of diterpene structure elucidation from 13 CNMR spectra. Diterpenes are organic compounds oflow molecular weight with a skeleton of 20 carbon atoms. They are of significant chemical and commercial interest because oftheir use as lead compounds in the search for new pharmaceutical effectors. The interpretation of diterpene 13 CNMR spectra normally requires specialists with detailed spectroscopic knowledge and substantial experience in natural products chemistry, specifically knowledge on peak patterns and chemical structures. Given a database ofpeak patterns for diterpenes with known structure, we apply several ILP approaches to discover correlations between peak patterns and chemical structure. The approaches used include first - order inductive learning, relational instance based learning, induction oflogical decision trees, and inductive constraint logic. Performance close to that of domain experts is achieved, which suffices for practical use.     

Evaluating the performance of a discrete manufacturing process using RFID: A case study

The material flow is a major focus point in improving productivity in today’s product diversified manufacturing organizations. Nevertheless, there is still a lack of efficient methods to control material flow through manufacturing processes in cases where multi-item tracking is difficult to achieve. This paper presents an RFID-based RTLS (Real-Time Location System) solution for obtaining multi-item work-in-process visibility within a manufacturer. It delivers detailed performance metrics through RTLS data analysis in order to evaluate workflow performance and to obtain a lean process. We pre-filter the RTLS data through the development of a middleware data collection method to acquire near real-time performance evaluation. A case study illustrates the complete process including measurements before and after a workflow redesign. The increased level of detail from RFID measurements yields new insights into shop floor actions and the real effects of redesign efforts.     

Comparison of Some Entropy Conservative Numerical Fluxes for the Euler Equations

Entropy conservation and stability of numerical methods in gas dynamics have received much interest. Entropy conservative numerical fluxes can be used as ingredients in two kinds of schemes: firstly, as building blocks in the subcell flux differencing form of Fisher and Carpenter (Technical Report NASA/TM-2013-217971, NASA, 2013; J Comput Phys 252:518–557, 2013) and secondly (enhanced by dissipation) as numerical surface fluxes in finite volume like schemes. The purpose of this article is threefold. Firstly, the flux differencing theory is extended, guaranteeing high-order for general symmetric and consistent numerical fluxes and investigating entropy stability in a generalised framework of summation-by-parts operators applicable to multiple dimensions and simplex elements. Secondly, a general procedure to construct affordable entropy conservative fluxes is described explicitly and used to derive several new fluxes. Finally, robustness properties of entropy stable numerical fluxes are investigated and positivity preservation is proven for several entropy conservative fluxes enhanced with local Lax–Friedrichs type dissipation operators. All these theoretical investigations are supplemented with numerical experiments.     

Autonomic information flow improves prognostic impact of task force HRV monitoring

Heart rate variability (HRV) represents the cardiovascular control mediated by the autonomic nervous system and other mechanisms. In the established task force HRV monitoring different cardiovascular control mechanisms can approximately be identified at typical frequencies of heart rate oscillations by power spectral analysis. HRV measures assessing complex and fractal behavior partly improved clinical risk stratification. However, their relationship to (patho-)physiology is not sufficiently explored. Objective of the present work is the introduction of complexity measures of different physiologically relevant time scales. This is achieved by a new concept of the autonomic information flow (AIF) analysis which was designed according to task force HRV. First applications show that different time scales of AIF improve the risk stratification of patients with multiple organ dysfunction syndrome and cardiac arrest patients in comparison to standard HRV. Each group's significant time scales correspond to their respective pathomechanisms.     

An efficient microfluidic sorter: implementation of double meandering micro striplines for magnetic particles switching

The ability to trap, manipulate, and separate magnetic beads has become one of the key requirements in realizing an integrated magnetic lab-on-chip biosensing system. In this article, we present the design and fabrication of an integrated magneto-fluidic device for sorting magnetic particles with a sorting efficiency of up to 95%. The actuation and manipulation of magnetic beads are realized using microfabricated square meandering current-carrying micro striplines. The current is alternated between two neighboring micro striplines to switch the magnetic beads to either one of the two outlets. We performed a series of parametric study to investigate the effect of applied current, flow rate, and switching frequency on the sorting efficiency. Experimental results reveal that the sorting efficiency is proportional to the square of current applied to the stripline, and decreases with increasing buffer flow rate and switching frequency. Such phenomena agree well with our theoretical analysis and simulation result. The fastest switching rate, which is limited by the microchannel geometry and bead velocity, is 2 Hz.     

Collision Detection for Deformable Objects

Interactive environments for dynamically deforming objects play an important role in surgery simulation and entertainment technology. These environments require fast deformable models and very efficient collision handling techniques. While collision detection for rigid bodies is well investigated, collision detection for deformable objects introduces additional challenging problems. This paper focuses on these aspects and summarizes recent research in the area of deformable collision detection. Various approaches based on bounding volume hierarchies, distance fields and spatial partitioning are discussed. In addition, image‐space techniques and stochastic methods are considered. Applications in cloth modeling and surgical simulation are presented.