Numerical studies of the performance of an optimally controlled nonlinear stochastic oscillator

This paper deals with the optimal control of a random nonlinear triangular wave oscillator. It is assumed that the oscillator is subjected to two different kinds of perturbation — the first kind is represented by a vector of independent standard Wiener processes and the second kind by a generalized type of a Poisson process.Sufficient conditions on the optimal controls are derived. These conditions require the existence of a smooth solution to a certain nonlinear partial integrodifferential equation. Numerical procedures for the solution of this equation are suggested. The performance of the controlled random oscillator is investigated via the numerical solutions to the nonlinear partial integrodifferential equation. Also, the performance of the random oscillator in the case where no control is applied is studied by means of the numerical solutions to a linear partial integrodifferential equation.     

An in vivo ultrasonic model of liver parenchyma

Several ultrasonic tissue characterization features are known to discriminate pathological from normal tissue in vivo. Previously, the authors developed an in vivo attenuation- and backscatter estimation method with each frequency dependent coefficient being reduced to a slope and intercept at central frequency. They derived expressions predicting the standard deviation (SD) of these features, assuming a commonly used ultrasonic model of liver parenchyma. In its application to in vivo data, the SD of the intercept features was unexpectedly high. Another feature, signal-to-noise ratio (SNR), showed a significant bias related to the window size. In this paper, the model is extended with the notion of inhomogeneous parenchyma background (IPB). IPB is shown to be present in normal liver parenchyma and is statistically described by a noise term with small amplitude and large correlation cell size. A method is presented to estimate the IPB characteristics. The expressions predicting SD are extended, and an expression is derived predicting the window size bias of the feature SNR. The accuracy and precision estimated from a large in vivo data set shows good agreement with the predictions with the extended model. It is concluded that IPB is a realistic and relevant phenomenon and should be part of the in vivo ultrasonic model of liver parenchyma.     

Sylvan: multi-core framework for decision diagrams

Decision diagrams, such as binary decision diagrams, multi-terminal binary decision diagrams and multi-valued decision diagrams, play an important role in various fields. They are especially useful to represent the characteristic function of sets of states and transitions in symbolic model checking. Most implementations of decision diagrams do not parallelize the decision diagram operations. As performance gains in the current era now mostly come from parallel processing, an ongoing challenge is to develop datastructures and algorithms for modern multi-core architectures. The decision diagram package Sylvan provides a contribution by implementing parallelized decision diagram operations and thus allowing sequential algorithms that use decision diagrams to exploit the power of multi-core machines. This paper discusses the design and implementation of Sylvan, especially an improvement to the lock-free unique table that uses bit arrays, the concurrent operation cache and the implementation of parallel garbage collection. We extend Sylvan with multi-terminal binary decision diagrams for integers, real numbers and rational numbers. This extension also allows for custom MTBDD leaves and operations and we provide an example implementation of GMP rational numbers. Furthermore, we show how the provided framework can be integrated in existing tools to provide out-of-the-box parallel BDD algorithms, as well as support for the parallelization of higher-level algorithms. As a case study, we parallelize on-the-fly symbolic reachability in the model checking toolset LTSmin. We experimentally demonstrate that the parallelization of symbolic model checking for explicit-state modeling languages, as supported by LTSmin, scales well. We also show that improvements in the design of the unique table result in faster execution of on-the-fly symbolic reachability.     

Solving scheduling problems by untimed model checking

In this article, we show how scheduling problems can be modelled in untimed process algebra, by using special tick actions. A minimal-cost trace leading to a particular action, is one that minimises the number of tick steps. As a result, we can use any (timed or untimed) model checking tool to find shortest schedules. Instantiating this scheme to μCRL, we profit from a richer specification language than timed model checkers usually offer. Also, we can profit from efficient distributed state space generators. We propose a variant of breadth-first search that visits all states between consecutive tick steps, before moving to the next time slice. We experimented with a sequential and a distributed implementation of this algorithm. In addition, we experimented with beam search, which visits only parts of the search space, to find near-optimal solutions. Our approach is applied to find optimal schedules for test batches of a realistic clinical chemical analyser, which performs several kinds of tests on patient samples.     

The TaPaSCo Open-Source Toolflow

The integration of FPGA-based accelerators into a complete heterogeneous system is a challenging task faced by many researchers and engineers, especially now that FPGAs enjoy increasing popularity as implementation platforms for efficient, application-specific accelerators for domains such as signal processing, machine learning and intelligent storage. To lighten the burden of system integration from the developers of accelerators, the open-source TaPaSCo framework presented in this work provides an automated toolflow for the construction of heterogeneous many-core architectures from custom processing elements, and a simple, uniform programming interface to utilize spatially distributed, parallel computation on FPGAs. TaPaSCo aims to increase the scalability and portability of FPGA designs through automated design space exploration, greatly simplifying the scaling of hardware designs and facilitating iterative growth and portability across FPGA devices and families. This work describes TaPaSCo with its primary design abstractions and shows how TaPaSCo addresses portability and extensibility of FPGA hardware designs for systems-on-chip. A study of successful projects using TaPaSCo shows its versatility and can serve as inspiration and reference for future users, with more details on the usage of TaPaSCo presented in an in-depth case study and a short overview of the workflow.

     

Selective suppression of harmful cyanobacteria in an entire lake with hydrogen peroxide

Although harmful cyanobacteria form a major threat to water quality, few methods exist for the rapid suppression of cyanobacterial blooms. Since laboratory studies indicated that cyanobacteria are more sensitive to hydrogen peroxide (H₂O₂) than eukaryotic phytoplankton, we tested the application of H₂O₂ in natural waters. First, we exposed water samples from a recreational lake dominated by the toxic cyanobacterium Planktothrix agardhii to dilute H₂O₂. This reduced the photosynthetic vitality by more than 70% within a few hours. Next, we installed experimental enclosures in the lake, which revealed that H₂O₂ selectively killed the cyanobacteria without major impacts on eukaryotic phytoplankton, zooplankton, or macrofauna. Based on these tests, we introduced 2 mg L⁻¹ (60 μM) of H₂O₂ homogeneously into the entire water volume of the lake with a special dispersal device, called the water harrow. The cyanobacterial population as well as the microcystin concentration collapsed by 99% within a few days. Eukaryotic phytoplankton (including green algae, cryptophytes, chrysophytes and diatoms), zooplankton and macrofauna remained largely unaffected. Following the treatment, cyanobacterial abundances remained low for 7 weeks. Based on these results, we propose the use of dilute H₂O₂ for the selective elimination of harmful cyanobacteria from recreational lakes and drinking water reservoirs, especially when immediate action is urgent and/or cyanobacterial control by reduction of eutrophication is currently not feasible. A key advantage of this method is that the added H₂O₂ degrades to water and oxygen within a few days, and thus leaves no long-term chemical traces in the environment.     

Mitogen-Activated Protein (MAP) Kinases in Plant Metal Stress: Regulation and Responses in Comparison to Other Biotic and Abiotic Stresses

Exposure of plants to toxic concentrations of metals leads to disruption of the cellular redox status followed by an accumulation of reactive oxygen species (ROS). ROS, like hydrogen peroxide, can act as signaling molecules in the cell and induce signaling via mitogen-activated protein kinase (MAPK) cascades. MAPK cascades are evolutionary conserved signal transduction modules, able to convert extracellular signals to appropriate cellular responses. In this review, our current understanding about MAPK signaling in plant metal stress is discussed. However, this knowledge is scarce compared to research into the role of MAPK signaling in the case of other abiotic and biotic stresses. ROS production is a common response induced by different stresses and undiscovered analogies may exist with metal stress. Therefore, further attention is given to MAPK signaling in other biotic and abiotic stresses and its interplay with other signaling pathways to create a framework in which the involvement of MAPK signaling in metal stress may be studied.     

A comparison of five heuristics for the multiple depot vehicle scheduling problem

Given a set of timetabled tasks, the multi-depot vehicle scheduling problem consists of determining least-cost schedules for vehicles assigned to several depots such that each task is accomplished exactly once by a vehicle. In this paper, we propose to compare the performance of five different heuristics for this well-known problem, namely, a truncated branch-and-cut method, a Lagrangian heuristic, a truncated column generation method, a large neighborhood search heuristic using truncated column generation for neighborhood evaluation, and a tabu search heuristic. The first three methods are adaptations of existing methods, while the last two are new in the context of this problem. Computational results on randomly generated instances show that the column generation heuristic performs the best when enough computational time is available and stability is required, while the large neighborhood search method is the best alternative when looking for good quality solutions in relatively fast computational times.